system

Abstract

The system according to this embodiment aims to optimize the robot's movements by linking images of objects with tactile information. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, an acquisition unit, a generation unit, and an operation unit. The collection unit collects images and tactile information of an object. The learning unit learns the images and tactile information collected by the collection unit. The acquisition unit acquires images of an object based on the data learned by the learning unit. The generation unit generates tactile information based on the images acquired by the acquisition unit. The operation unit performs robot operations based on the tactile information generated by the generation unit.

Description

Technical Field

[0002]

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for a robot to effectively cooperate the image of an object and tactile information for operation, and there is room for improvement.

[0005] The system according to an embodiment aims to optimize the operation of a robot by coordinating the image of an object and tactile information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a learning unit, an acquisition unit, a generation unit, and an operation unit. The collection unit collects images and tactile information of an object. The learning unit learns the images and tactile information collected by the collection unit. The acquisition unit acquires images of an object based on the data learned by the learning unit. The generation unit generates tactile information based on the images acquired by the acquisition unit. The operation unit performs robotic actions based on the tactile information generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize the robot's movements by linking images of objects with tactile information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that allows a robot to understand the tactile information of an object and reflect it in its actions by linking the image of the object with tactile information. This system collects, learns, acquires, generates, and reflects the image and tactile information of an object in its actions, enabling the robot to understand the tactile information of an object and achieve actions that are closer to those of a human. For example, in the learning phase, tactile information such as the hardness, temperature, and texture of the object's surface is acquired by sensors, and at the same time, an image of the object is taken with a camera. This data is input into the AI, which links the image with the tactile information. At this time, the AI ​​learns the relationship between the image and the tactile information and builds a model that estimates the tactile information from the image of the object. Next, in the input phase, the robot acquires an image of the object. For example, the robot takes an image of the object using a camera. This image is input into the model built in the learning phase, and tactile information of the object is generated. For example, when the robot handles a hard object, it can adjust the amount of force applied according to its hardness. Finally, the generated tactile information is reflected in the actions the robot takes when handling the object. For example, when the robot handles a soft object, it can perform gentle actions according to its softness. Furthermore, when a robot handles a warm object, it can perform appropriate actions according to its temperature. In this way, the robot can understand the tactile information of an object and achieve actions that are closer to those of a human.

[0029] The system according to this embodiment comprises a collection unit, a learning unit, an acquisition unit, a generation unit, and an operation unit. The collection unit collects images and tactile information of an object. The collection unit, for example, acquires tactile information such as the hardness, temperature, and texture of the object's surface using sensors, and simultaneously captures images of the object with a camera. The collection unit, for example, measures the hardness of the object using a pressure sensor and measures the temperature of the object using a temperature sensor. The collection unit can also, for example, capture the texture of the object's surface using a camera. The learning unit learns the images and tactile information collected by the collection unit. The learning unit, for example, uses AI to link images and tactile information. The AI ​​can, for example, use deep learning technology to learn the relationship between images and tactile information. The acquisition unit acquires images of the object based on the data learned by the learning unit. The acquisition unit, for example, uses a robot to capture images of the object using a camera. The acquisition unit can, for example, acquire detailed images of the object using a high-resolution camera. The generation unit generates tactile information based on the images acquired by the acquisition unit. The generation unit generates tactile information of an object based on a model built in the learning phase, for example. The generation unit can generate tactile information of an object with high accuracy using AI, for example. The operation unit performs robot movements based on the tactile information generated by the generation unit. The operation unit controls the robot's movements based on the generated tactile information, for example. The operation unit can adjust the movement of the robot's joints and perform actions according to the tactile information of the object, for example. As a result, the system according to this embodiment can understand the tactile information of an object and achieve more human-like movements.

[0030] The data collection unit can acquire tactile information such as the hardness, temperature, and texture of an object's surface using sensors, and simultaneously capture images of the object with a camera. For example, the data collection unit can measure the hardness of an object's surface with a pressure sensor, measure its temperature with a temperature sensor, and capture its texture with a camera. For example, the data collection unit can measure the hardness of an object with a pressure sensor and collect that data. It can also measure the temperature of an object with a temperature sensor and collect that data. It can also capture images of an object's texture with a camera and collect that image data. By simultaneously collecting tactile information and images of an object, more accurate data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the hardness data of an object into an AI and have the AI ​​perform a hardness analysis.

[0031] The learning unit can link images and tactile information using AI. For example, the learning unit can use deep learning techniques to link images and tactile information. For example, the learning unit can use a neural network to learn the relationship between images and tactile information. Furthermore, the learning unit can use large amounts of data to learn the relationship between images and tactile information with high accuracy. For example, the learning unit can use thousands of images and their corresponding tactile information as training data. The learning unit can also combine different algorithms to learn the relationship between images and tactile information. For example, the learning unit can use a combination of deep learning and support vector machines (SVMs). This allows the AI ​​to learn the relationship between images and tactile information with high accuracy. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without generative AI. For example, the learning unit can input data into a generative AI to learn the relationship between images and tactile information, and have the generative AI perform the learning.

[0032] The acquisition unit allows the robot to capture images of objects using a camera. For example, the acquisition unit allows the robot to capture images of objects using a high-resolution camera. For example, the acquisition unit can use a high-resolution camera to acquire detailed images of objects. The acquisition unit can also adjust the position and angle of the camera to accurately acquire images of objects. For example, the acquisition unit can adjust the camera position to capture an overall image of an object. The acquisition unit can also adjust the camera angle to capture detailed images of specific parts of an object. This allows the robot to accurately acquire images of objects using a camera. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input image data captured by the camera into an AI and have the AI ​​perform image analysis.

[0033] The generation unit can generate tactile information of an object based on the model built in the learning phase. For example, the generation unit can generate tactile information of an object with high accuracy using AI. The generation unit can also combine different algorithms to generate tactile information of an object. For example, the generation unit can use a combination of deep learning and support vector machines (SVM). The generation unit can also use a large amount of data to generate tactile information of an object. For example, the generation unit can use thousands of images and their corresponding tactile information as training data. This allows for the generation of tactile information of an object with high accuracy using the model built in the learning phase. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input the model built in the learning phase into a generative AI and have the generative AI perform the generation of tactile information.

[0034] The motion unit can perform actions when the robot handles an object based on the generated tactile information. For example, the motion unit can control the robot's movements based on the generated tactile information. For example, the motion unit can adjust the movement of the robot's joints and perform actions according to the tactile information of the object. The motion unit can also combine different algorithms to control the robot's movements. For example, the motion unit can use a combination of deep learning and reinforcement learning. The motion unit can also use large amounts of data to control the robot's movements. For example, the motion unit can optimize the robot's movements using thousands of simulation data. This allows the robot to perform appropriate actions based on the generated tactile information, resulting in movements that are closer to those of a human. Some or all of the above-described processes in the motion unit may be performed using, for example, generative AI, or not using generative AI. For example, the motion unit can input the generated tactile information into a generative AI and have the generative AI perform the robot's movement control.

[0035] The data collection unit can collect not only the hardness, temperature, and texture of an object's surface, but also its vibration and acoustic characteristics. For example, the data collection unit can collect the acoustic characteristics of an object when it is struck, simultaneously with the hardness of its surface. For example, the data collection unit can measure the hardness of an object with a pressure sensor and collect the data. It can also collect the acoustic characteristics of an object when it is struck with a microphone and analyze the data. Furthermore, the data collection unit can collect the vibration characteristics of an object when it is vibrated, simultaneously with its temperature. For example, the data collection unit can measure the temperature of an object with a temperature sensor and collect the data. It can also collect the vibration characteristics of an object when it is vibrated with an acceleration sensor and analyze the data. By collecting the vibration and acoustic characteristics of an object, a wider variety of tactile information can be obtained. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the vibration characteristics data of an object into an AI and have the AI ​​perform the analysis of the vibration characteristics.

[0036] The data collection unit can use different sensors depending on the shape and dimensions of the object during collection. For example, the data collection unit can use a high-precision tactile sensor for small objects to collect detailed tactile information. For example, the data collection unit can measure the hardness of the surface of a small object with a high-precision pressure sensor and collect the data. The data collection unit can also use a wide-area tactile sensor for large objects to collect overall tactile information. For example, the data collection unit can measure the hardness of the surface of a large object with a wide-area pressure sensor and collect the data. The data collection unit can also use multi-directional tactile sensors for objects with complex shapes to collect comprehensive tactile information. For example, the data collection unit can measure the hardness of the surface of an object with a complex shape from multiple directions and collect the data. This allows for the collection of more accurate tactile information by using appropriate sensors according to the shape and size of the object. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the object's shape data into AI and have the AI ​​perform shape analysis.

[0037] The data collection unit can simultaneously collect information about the surrounding environment of an object during data collection. For example, the data collection unit can collect ambient humidity along with tactile information of the object. For example, the data collection unit can measure the hardness of the object's surface with a pressure sensor and collect that data. The data collection unit can also measure ambient humidity with a humidity sensor and collect that data. Furthermore, the data collection unit can collect ambient illuminance along with tactile information of the object. For example, the data collection unit can measure the hardness of the object's surface with a pressure sensor and collect that data. The data collection unit can also measure ambient illuminance with an illuminance sensor and collect that data. By simultaneously collecting information about the surrounding environment of the object, a wider variety of data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the surrounding environment data of the object into AI and have AI perform analysis of the environmental information.

[0038] The data collection unit can also acquire the object's usage history and information on changes over time during data collection. For example, the data collection unit can collect the object's usage history simultaneously with its tactile information. For example, the data collection unit can measure the hardness of the object's surface with a pressure sensor and collect that data. The data collection unit can also retrieve the object's usage history from a database and collect that data. The data collection unit can also collect information on changes over time simultaneously with the object's tactile information. For example, the data collection unit can measure the hardness of the object's surface with a pressure sensor and collect that data. The data collection unit can also retrieve information on changes over time from a database and collect that data. By acquiring the object's usage history and information on changes over time, more detailed data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the object's usage history data into AI and have AI perform the history analysis.

[0039] The learning unit can use images taken from different angles and under different lighting conditions during learning to strengthen the relationship between the image of an object and its tactile information. For example, the learning unit can photograph the object from different angles and learn the relationship between the image and its tactile information. The learning unit can also photograph the object under different lighting conditions and learn the relationship between the image and its tactile information. For example, the learning unit can use images taken of the object under different lighting conditions and learn the relationship between the image and its tactile information. The learning unit can also photograph the object from different distances and learn the relationship between the image and its tactile information. For example, the learning unit can use images taken of the object from different distances and learn the relationship between the image and its tactile information. This allows the relationship between the image of an object and its tactile information to be strengthened by using images from different angles and under different lighting conditions. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit can input image data taken at different angles and under different lighting conditions into the generating AI, allowing the generating AI to perform correlation learning.

[0040] The learning unit can analyze the fluctuation patterns of an object's tactile information during training and construct a dynamic tactile information model. For example, the learning unit can analyze the temporal fluctuations of an object's tactile information and construct a dynamic model. For example, the learning unit can analyze the temporal fluctuations of an object's tactile information and construct a dynamic tactile information model based on that data. The learning unit can also analyze the temperature changes of an object's tactile information and construct a dynamic model. For example, the learning unit can analyze the temperature changes of an object's tactile information and construct a dynamic tactile information model based on that data. The learning unit can also analyze the pressure changes of an object's tactile information and construct a dynamic model. For example, the learning unit can analyze the pressure changes of an object's tactile information and construct a dynamic tactile information model based on that data. In this way, a dynamic tactile information model can be constructed by analyzing the fluctuation patterns of an object's tactile information. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input variable data of tactile information of an object into the generating AI, allowing the generating AI to construct a dynamic model.

[0041] The learning unit can learn not only the image and tactile information of an object, but also the usage scenario information of that object during the learning process. For example, the learning unit can learn the usage scenario information of an object simultaneously with the tactile information of that object. For example, the learning unit can measure the hardness of the surface of an object with a pressure sensor and collect that data. The learning unit can also obtain the usage scenario information of an object from a database and learn that data. Furthermore, the learning unit can learn the usage scenario information of an object simultaneously with the image of that object. For example, the learning unit can take an image of an object with a camera and learn the image data and the usage scenario information. The learning unit can also learn the relationship between the usage scenario information of an object and tactile information. For example, the learning unit can obtain the usage scenario information of an object from a database and learn the relationship between that data and tactile information. By learning the usage scenario information of an object, a wider variety of data can be obtained. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input the usage scenario data of an object into a generative AI and have the generative AI perform relationship learning.

[0042] The learning unit can extract commonalities in tactile information between different object categories during training and construct a general-purpose tactile model. For example, the learning unit can extract commonalities in hardness between different object categories and construct a general-purpose tactile model. For example, the learning unit can collect hardness data between different object categories and extract its commonalities. The learning unit can also extract commonalities in temperature between different object categories and construct a general-purpose tactile model. For example, the learning unit can collect temperature data between different object categories and extract its commonalities. The learning unit can also extract commonalities in texture between different object categories and construct a general-purpose tactile model. For example, the learning unit can collect texture data between different object categories and extract its commonalities. In this way, a general-purpose tactile model can be constructed by extracting commonalities in tactile information between different object categories. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit can input tactile data from different object categories into a generating AI and have the generating AI extract commonalities.

[0043] The acquisition unit can track the movement and deformation of an object in real time during acquisition and acquire continuous image data. For example, the acquisition unit can track the movement of an object in real time and acquire continuous image data. For example, the acquisition unit can use a camera and a motion tracking algorithm to track the movement of an object in real time. The acquisition unit can also track the deformation of an object in real time and acquire continuous image data. For example, the acquisition unit can use a camera and a deformation tracking algorithm to track the deformation of an object in real time. The acquisition unit can also track the rotation of an object in real time and acquire continuous image data. For example, the acquisition unit can use a camera and a rotation tracking algorithm to track the rotation of an object in real time. In this way, continuous image data can be acquired by tracking the movement and deformation of an object in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the object's movement data into AI and have AI perform the motion analysis.

[0044] The acquisition unit can perform image processing during acquisition to remove background information from objects and enhance their contours. For example, the acquisition unit can use an image processing algorithm to remove background information from objects. The acquisition unit can also use an edge detection algorithm to enhance the contours of objects. The acquisition unit can also perform image processing to enhance the colors of objects and clarify their contours. For example, the acquisition unit can use a color enhancement algorithm to enhance the colors of objects. The acquisition unit can also perform image processing to remove shadows from objects and enhance their contours. For example, the acquisition unit can use a shadow removal algorithm to remove shadows from objects. This allows for the acquisition of clearer image data by removing background information from objects and enhancing their contours. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input image data of objects into AI and have the AI ​​perform background information removal and contour enhancement.

[0045] The acquisition unit can acquire not only an image of an object but also 3D model data of the object during acquisition. For example, the acquisition unit can acquire 3D model data of an object simultaneously with an image of the object. For example, the acquisition unit can take an image of an object with a camera and acquire 3D model data simultaneously with the image data. The acquisition unit can also generate 3D model data of an object based on an image of the object. For example, the acquisition unit can input the image data of an object into 3D modeling software and generate 3D model data. The acquisition unit can also acquire 3D model data of an object and integrate it with the image. For example, the acquisition unit can acquire 3D model data of an object from a database and integrate that data with the image data. By acquiring 3D model data in addition to the image of the object, more detailed data can be obtained. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the 3D model data of an object into AI and have the AI ​​perform model generation.

[0046] The acquisition unit can acquire multispectral image data by capturing images of an object with light of different wavelengths during acquisition. For example, the acquisition unit can capture images of an object with visible light and acquire multispectral image data. For example, the acquisition unit can capture images of an object with visible light and collect the data. The acquisition unit can also capture images of an object with infrared light and acquire multispectral image data. For example, the acquisition unit can capture images of an object with infrared light and collect the data. The acquisition unit can also capture images of an object with ultraviolet light and acquire multispectral image data. For example, the acquisition unit can capture images of an object with ultraviolet light and collect the data. In this way, multispectral image data can be acquired by capturing images with light of different wavelengths. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the multispectral image data of an object into AI and have AI perform data analysis.

[0047] The generation unit can generate not only the tactile information of an object, but also its coefficient of friction and elasticity during the generation process. For example, the generation unit can generate the coefficient of friction simultaneously with the tactile information of an object. For example, when generating the tactile information of an object, the generation unit can also generate data on the coefficient of friction. The generation unit can also generate the coefficient of elasticity simultaneously with the tactile information of an object. For example, when generating the tactile information of an object, the generation unit can also generate data on the coefficient of elasticity. The generation unit can also generate both the coefficient of friction and the coefficient of elasticity simultaneously with the tactile information of an object. For example, when generating the tactile information of an object, the generation unit can also generate data on the coefficient of friction and the coefficient of elasticity. By generating the coefficient of friction and the coefficient of elasticity of an object, more detailed tactile information can be obtained. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the coefficient of friction data of an object into AI and have AI perform the data generation.

[0048] The generation unit can change the tactile information of an object over time during generation, providing dynamic tactile feedback. For example, the generation unit can change the tactile information of an object over time and provide dynamic feedback based on that data. The generation unit can also change the tactile information of an object in response to temperature changes and provide dynamic feedback. For example, the generation unit can change the tactile information of an object in response to temperature changes and provide dynamic feedback based on that data. Furthermore, the generation unit can change the tactile information of an object in response to pressure changes and provide dynamic feedback. For example, the generation unit can change the tactile information of an object in response to pressure changes and provide dynamic feedback based on that data. This allows for dynamic tactile feedback to be provided by changing the tactile information of an object over time. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input fluctuation data of the object's tactile information into the AI ​​and have the AI ​​generate dynamic feedback.

[0049] The generation unit can generate not only tactile information of an object but also temperature change information of an object during the generation process. For example, the generation unit can generate temperature change information simultaneously with the tactile information of an object. For example, when generating tactile information of an object, the generation unit can also generate temperature change data. The generation unit can also generate the rate of temperature change simultaneously with the tactile information of an object. For example, when generating tactile information of an object, the generation unit can also generate data on the rate of temperature change. The generation unit can also generate the range of temperature change simultaneously with the tactile information of an object. For example, when generating tactile information of an object, the generation unit can also generate data on the range of temperature change. By generating temperature change information of an object, more detailed tactile information can be obtained. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input temperature change data of an object into AI and have AI perform the data generation.

[0050] The generation unit can perform conversion processing to adapt the tactile information of an object to different tactile devices during generation. For example, the generation unit can perform conversion processing to adapt the tactile information of an object to a vibration device. For example, the generation unit can use a conversion algorithm to adapt the tactile information of an object to a vibration device. The generation unit can also perform conversion processing to adapt the tactile information of an object to a temperature device. For example, the generation unit can use a conversion algorithm to adapt the tactile information of an object to a temperature device. The generation unit can also perform conversion processing to adapt the tactile information of an object to a pressure device. For example, the generation unit can use a conversion algorithm to adapt the tactile information of an object to a pressure device. By adapting the tactile information of an object to different tactile devices, a wider variety of tactile feedback can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the tactile information of an object into AI and have AI perform the conversion to adapt to the device.

[0051] The motion unit can adjust the flexibility of the robot's joints based on tactile information from an object during operation. For example, if the object is soft, the motion unit can increase the flexibility of the robot's joints. For example, if the object is soft, the motion unit can use a joint control algorithm to increase the flexibility of the robot's joints. The motion unit can also decrease the flexibility of the robot's joints if the object is hard. For example, if the object is hard, the motion unit can use a joint control algorithm to decrease the flexibility of the robot's joints. The motion unit can also set the flexibility of the robot's joints to a moderate level if the object is warm. For example, if the object is warm, the motion unit can use a joint control algorithm to set the flexibility of the robot's joints to a moderate level. By adjusting the flexibility of the robot's joints based on tactile information from an object, more appropriate operation can be achieved. Some or all of the above processing in the motion unit may be performed using AI, for example, or without AI. For example, the motion unit can input tactile information from an object into AI and have the AI ​​perform the adjustment of the joint flexibility.

[0052] The operating unit can dynamically change the robot's grip force based on tactile information from an object during operation. For example, if the object is soft, the operating unit can weaken the robot's grip force. For example, the operating unit can use a grip force control algorithm to weaken the robot's grip force when the object is soft. The operating unit can also strengthen the robot's grip force when the object is hard. For example, the operating unit can use a grip force control algorithm to strengthen the robot's grip force when the object is hard. The operating unit can also set the robot's grip force to a moderate level when the object is warm. For example, the operating unit can use a grip force control algorithm to set the robot's grip force to a moderate level when the object is warm. This allows for more appropriate operation by dynamically changing the robot's grip force based on tactile information from an object. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input tactile information from an object into the AI ​​and have the AI ​​adjust the grip force.

[0053] The operating unit can limit the robot's range of motion based on tactile information from an object during operation. For example, if the object is soft, the operating unit can narrow the robot's range of motion. For example, if the object is soft, the operating unit can use a range of motion control algorithm to narrow the robot's range of motion. The operating unit can also widen the robot's range of motion if the object is hard. For example, if the object is hard, the operating unit can use a range of motion control algorithm to widen the robot's range of motion. The operating unit can also moderate the robot's range of motion if the object is warm. For example, if the object is warm, the operating unit can use a range of motion control algorithm to moderate the robot's range of motion. This allows for more appropriate operation by limiting the robot's range of motion based on tactile information from an object. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input tactile information from an object into the AI ​​and have the AI ​​adjust the range of motion.

[0054] The motion unit can predict the robot's movement based on tactile information from an object during operation and create a motion plan in advance. For example, if the object is soft, the motion unit can predict the robot's movement and create a soft motion plan. For example, if the object is soft, the motion unit can use a motion prediction algorithm to predict the robot's movement and create a soft motion plan. The motion unit can also predict the robot's movement and create a hard motion plan if the object is hard. For example, if the object is hard, the motion unit can use a motion prediction algorithm to predict the robot's movement and create a hard motion plan. The motion unit can also predict the robot's movement and create a warm motion plan if the object is warm. For example, if the object is warm, the motion unit can use a motion prediction algorithm to predict the robot's movement and create a warm motion plan. This allows for more appropriate operation by predicting the robot's movement based on tactile information from an object and creating a motion plan in advance. Some or all of the above processing in the motion unit may be performed using AI, for example, or without AI. For example, the motion unit can input tactile information of an object into the AI, allowing the AI ​​to plan and execute the motion.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can collect chemical composition information of an object simultaneously with collecting image and tactile information. For example, the data collection unit can measure the surface hardness of an object using a pressure sensor and collect the data. It can also analyze the chemical composition of an object using a spectrometer and collect the data. Furthermore, the data collection unit can measure the temperature of an object using a temperature sensor and collect the data. By simultaneously collecting chemical composition information, more detailed data can be obtained. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the chemical composition data of an object into an AI and have the AI ​​perform the analysis of the components.

[0057] The learning unit can learn information about the object's environment when learning its image and tactile information. For example, the learning unit can measure the hardness of the object's surface using a pressure sensor and collect that data. It can also obtain information about the object's environment from a database and learn that data. Furthermore, the learning unit can learn information about the environment simultaneously with the object's image. By learning information about the object's environment, it can obtain a wider variety of data. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the object's environment data into a generative AI and have the generative AI perform relevance learning.

[0058] The acquisition unit can acquire continuous image data by tracking the dynamic changes of an object in real time when acquiring an image of the object. For example, the acquisition unit can use a camera and a motion tracking algorithm to track the movement of an object in real time. The acquisition unit can also track the deformation of an object in real time and acquire continuous image data. Furthermore, the acquisition unit can track the rotation of an object in real time and acquire continuous image data. In this way, continuous image data can be acquired by tracking the movement and deformation of an object in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the object's motion data into AI and have AI perform motion analysis.

[0059] The generation unit can generate the friction coefficient and elastic modulus of an object when generating tactile information about that object. For example, the generation unit can generate friction coefficient data when generating tactile information about an object. Furthermore, the generation unit can generate the elastic modulus simultaneously with the tactile information of an object. In addition, the generation unit can generate both the friction coefficient and elastic modulus simultaneously with the tactile information of an object. This allows for the generation of more detailed tactile information by also generating the friction coefficient and elastic modulus of an object. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input friction coefficient data for an object into an AI and have the AI ​​generate the data.

[0060] The motion unit can predict the robot's movements based on tactile information from an object and create a motion plan in advance. For example, if the object is soft, the motion unit can use a motion prediction algorithm to predict the robot's movements and create a soft motion plan. The motion unit can also predict the robot's movements and create a hard motion plan if the object is hard. Furthermore, if the object is warm, the motion unit can predict the robot's movements and create a warm motion plan. This allows for more appropriate movements by predicting the robot's movements based on tactile information from an object and creating a motion plan in advance. Some or all of the above processing in the motion unit may be performed using AI, for example, or without AI. For example, the motion unit can input tactile information from an object into an AI and have the AI ​​create the motion plan.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects images and tactile information of the object. For example, the collection unit acquires tactile information such as the hardness, temperature, and texture of the object's surface using sensors, and simultaneously captures images of the object with a camera. For example, the collection unit measures the hardness of the object using a pressure sensor and measures the temperature of the object using a temperature sensor. The collection unit can also capture the texture of the object's surface using a camera. Step 2: The learning unit learns from the images and tactile information collected by the collection unit. The learning unit links images and tactile information, for example, using AI. The AI ​​can learn the relationship between images and tactile information, for example, using deep learning technology. Step 3: The acquisition unit acquires an image of an object based on the data learned by the learning unit. The acquisition unit can, for example, have a robot take an image of an object using a camera. The acquisition unit can, for example, acquire a detailed image of an object using a high-resolution camera. Step 4: The generation unit generates tactile information based on the images acquired by the acquisition unit. The generation unit generates tactile information of an object based on, for example, the model built in the learning phase. The generation unit can generate tactile information of an object with high accuracy using, for example, AI. Step 5: The operating unit performs robotic actions based on the tactile information generated by the generating unit. For example, the operating unit controls the robot's actions based on the generated tactile information. For example, the operating unit can adjust the movement of the robot's joints and perform actions in accordance with the tactile information of an object.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that allows a robot to understand the tactile information of an object and reflect it in its actions by linking the image of the object with tactile information. This system collects, learns, acquires, generates, and reflects the image and tactile information of an object in its actions, enabling the robot to understand the tactile information of an object and achieve actions that are closer to those of a human. For example, in the learning phase, tactile information such as the hardness, temperature, and texture of the object's surface is acquired by sensors, and at the same time, an image of the object is taken with a camera. This data is input into the AI, which links the image with the tactile information. At this time, the AI ​​learns the relationship between the image and the tactile information and builds a model that estimates the tactile information from the image of the object. Next, in the input phase, the robot acquires an image of the object. For example, the robot takes an image of the object using a camera. This image is input into the model built in the learning phase, and tactile information of the object is generated. For example, when the robot handles a hard object, it can adjust the amount of force applied according to its hardness. Finally, the generated tactile information is reflected in the actions the robot takes when handling the object. For example, when the robot handles a soft object, it can perform gentle actions according to its softness. Furthermore, when a robot handles a warm object, it can perform appropriate actions according to its temperature. In this way, the robot can understand the tactile information of an object and achieve actions that are closer to those of a human.

[0064] The system according to this embodiment comprises a collection unit, a learning unit, an acquisition unit, a generation unit, and an operation unit. The collection unit collects images and tactile information of an object. The collection unit, for example, acquires tactile information such as the hardness, temperature, and texture of the object's surface using sensors, and simultaneously captures images of the object with a camera. The collection unit, for example, measures the hardness of the object using a pressure sensor and measures the temperature of the object using a temperature sensor. The collection unit can also, for example, capture the texture of the object's surface using a camera. The learning unit learns the images and tactile information collected by the collection unit. The learning unit, for example, uses AI to link images and tactile information. The AI ​​can, for example, use deep learning technology to learn the relationship between images and tactile information. The acquisition unit acquires images of the object based on the data learned by the learning unit. The acquisition unit, for example, uses a robot to capture images of the object using a camera. The acquisition unit can, for example, acquire detailed images of the object using a high-resolution camera. The generation unit generates tactile information based on the images acquired by the acquisition unit. The generation unit generates tactile information of an object based on a model built in the learning phase, for example. The generation unit can generate tactile information of an object with high accuracy using AI, for example. The operation unit performs robot movements based on the tactile information generated by the generation unit. The operation unit controls the robot's movements based on the generated tactile information, for example. The operation unit can adjust the movement of the robot's joints and perform actions according to the tactile information of the object, for example. As a result, the system according to this embodiment can understand the tactile information of an object and achieve more human-like movements.

[0065] The data collection unit can acquire tactile information such as the hardness, temperature, and texture of an object's surface using sensors, and simultaneously capture images of the object with a camera. For example, the data collection unit can measure the hardness of an object's surface with a pressure sensor, measure its temperature with a temperature sensor, and capture its texture with a camera. For example, the data collection unit can measure the hardness of an object with a pressure sensor and collect that data. It can also measure the temperature of an object with a temperature sensor and collect that data. It can also capture images of an object's texture with a camera and collect that image data. By simultaneously collecting tactile information and images of an object, more accurate data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the hardness data of an object into an AI and have the AI ​​perform a hardness analysis.

[0066] The learning unit can link images and tactile information using AI. For example, the learning unit can use deep learning techniques to link images and tactile information. For example, the learning unit can use a neural network to learn the relationship between images and tactile information. Furthermore, the learning unit can use large amounts of data to learn the relationship between images and tactile information with high accuracy. For example, the learning unit can use thousands of images and their corresponding tactile information as training data. The learning unit can also combine different algorithms to learn the relationship between images and tactile information. For example, the learning unit can use a combination of deep learning and support vector machines (SVMs). This allows the AI ​​to learn the relationship between images and tactile information with high accuracy. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without generative AI. For example, the learning unit can input data into a generative AI to learn the relationship between images and tactile information, and have the generative AI perform the learning.

[0067] The acquisition unit allows the robot to capture images of objects using a camera. For example, the acquisition unit allows the robot to capture images of objects using a high-resolution camera. For example, the acquisition unit can use a high-resolution camera to acquire detailed images of objects. The acquisition unit can also adjust the position and angle of the camera to accurately acquire images of objects. For example, the acquisition unit can adjust the camera position to capture an overall image of an object. The acquisition unit can also adjust the camera angle to capture detailed images of specific parts of an object. This allows the robot to accurately acquire images of objects using a camera. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input image data captured by the camera into an AI and have the AI ​​perform image analysis.

[0068] The generation unit can generate tactile information of an object based on the model built in the learning phase. For example, the generation unit can generate tactile information of an object with high accuracy using AI. The generation unit can also combine different algorithms to generate tactile information of an object. For example, the generation unit can use a combination of deep learning and support vector machines (SVM). The generation unit can also use a large amount of data to generate tactile information of an object. For example, the generation unit can use thousands of images and their corresponding tactile information as training data. This allows for the generation of tactile information of an object with high accuracy using the model built in the learning phase. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input the model built in the learning phase into a generative AI and have the generative AI perform the generation of tactile information.

[0069] The motion unit can perform actions when the robot handles an object based on the generated tactile information. For example, the motion unit can control the robot's movements based on the generated tactile information. For example, the motion unit can adjust the movement of the robot's joints and perform actions according to the tactile information of the object. The motion unit can also combine different algorithms to control the robot's movements. For example, the motion unit can use a combination of deep learning and reinforcement learning. The motion unit can also use large amounts of data to control the robot's movements. For example, the motion unit can optimize the robot's movements using thousands of simulation data. This allows the robot to perform appropriate actions based on the generated tactile information, resulting in movements that are closer to those of a human. Some or all of the above-described processes in the motion unit may be performed using, for example, generative AI, or not using generative AI. For example, the motion unit can input the generated tactile information into a generative AI and have the generative AI perform the robot's movement control.

[0070] The collection unit can estimate the user's emotions and select objects to collect based on the estimated emotions. For example, the collection unit can prioritize collecting soft objects when the user is relaxed. It can also prioritize collecting hard objects when the user is excited. It can also prioritize collecting warm objects when the user is stressed. By selecting objects to collect according to the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0071] The data collection unit can collect not only the hardness, temperature, and texture of an object's surface, but also its vibration and acoustic characteristics. For example, the data collection unit can collect the acoustic characteristics of an object when it is struck, simultaneously with the hardness of its surface. For example, the data collection unit can measure the hardness of an object with a pressure sensor and collect the data. It can also collect the acoustic characteristics of an object when it is struck with a microphone and analyze the data. Furthermore, the data collection unit can collect the vibration characteristics of an object when it is vibrated, simultaneously with its temperature. For example, the data collection unit can measure the temperature of an object with a temperature sensor and collect the data. It can also collect the vibration characteristics of an object when it is vibrated with an acceleration sensor and analyze the data. By collecting the vibration and acoustic characteristics of an object, a wider variety of tactile information can be obtained. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the vibration characteristics data of an object into an AI and have the AI ​​perform the analysis of the vibration characteristics.

[0072] The data collection unit can use different sensors depending on the shape and dimensions of the object during collection. For example, the data collection unit can use a high-precision tactile sensor for small objects to collect detailed tactile information. For example, the data collection unit can measure the hardness of the surface of a small object with a high-precision pressure sensor and collect the data. The data collection unit can also use a wide-area tactile sensor for large objects to collect overall tactile information. For example, the data collection unit can measure the hardness of the surface of a large object with a wide-area pressure sensor and collect the data. The data collection unit can also use multi-directional tactile sensors for objects with complex shapes to collect comprehensive tactile information. For example, the data collection unit can measure the hardness of the surface of an object with a complex shape from multiple directions and collect the data. This allows for the collection of more accurate tactile information by using appropriate sensors according to the shape and size of the object. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the object's shape data into AI and have the AI ​​perform shape analysis.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect data slowly if the user is relaxed, or quickly if the user is in a hurry, or intermittently if the user is excited. By adjusting the timing of data collection according to the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0074] The data collection unit can simultaneously collect information about the surrounding environment of an object during data collection. For example, the data collection unit can collect ambient humidity along with tactile information of the object. For example, the data collection unit can measure the hardness of the object's surface with a pressure sensor and collect that data. The data collection unit can also measure ambient humidity with a humidity sensor and collect that data. Furthermore, the data collection unit can collect ambient illuminance along with tactile information of the object. For example, the data collection unit can measure the hardness of the object's surface with a pressure sensor and collect that data. The data collection unit can also measure ambient illuminance with an illuminance sensor and collect that data. By simultaneously collecting information about the surrounding environment of the object, a wider variety of data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the surrounding environment data of the object into AI and have AI perform analysis of the environmental information.

[0075] The data collection unit can also acquire the object's usage history and information on changes over time during data collection. For example, the data collection unit can collect the object's usage history simultaneously with its tactile information. For example, the data collection unit can measure the hardness of the object's surface with a pressure sensor and collect that data. The data collection unit can also retrieve the object's usage history from a database and collect that data. The data collection unit can also collect information on changes over time simultaneously with the object's tactile information. For example, the data collection unit can measure the hardness of the object's surface with a pressure sensor and collect that data. The data collection unit can also retrieve information on changes over time from a database and collect that data. By acquiring the object's usage history and information on changes over time, more detailed data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the object's usage history data into AI and have AI perform the history analysis.

[0076] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, the learning unit can prioritize learning data on soft objects when the user is relaxed. It can also prioritize learning data on hard objects when the user is excited. Furthermore, it can prioritize learning data on warm objects when the user is stressed. This allows for the learning of more appropriate data by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, or not. For example, the learning unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0077] The learning unit can use images taken from different angles and under different lighting conditions during learning to strengthen the relationship between the image of an object and its tactile information. For example, the learning unit can photograph the object from different angles and learn the relationship between the image and its tactile information. The learning unit can also photograph the object under different lighting conditions and learn the relationship between the image and its tactile information. For example, the learning unit can use images taken of the object under different lighting conditions and learn the relationship between the image and its tactile information. The learning unit can also photograph the object from different distances and learn the relationship between the image and its tactile information. For example, the learning unit can use images taken of the object from different distances and learn the relationship between the image and its tactile information. This allows the relationship between the image of an object and its tactile information to be strengthened by using images from different angles and under different lighting conditions. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit can input image data taken at different angles and under different lighting conditions into the generating AI, allowing the generating AI to perform correlation learning.

[0078] The learning unit can analyze the fluctuation patterns of an object's tactile information during training and construct a dynamic tactile information model. For example, the learning unit can analyze the temporal fluctuations of an object's tactile information and construct a dynamic model. For example, the learning unit can analyze the temporal fluctuations of an object's tactile information and construct a dynamic tactile information model based on that data. The learning unit can also analyze the temperature changes of an object's tactile information and construct a dynamic model. For example, the learning unit can analyze the temperature changes of an object's tactile information and construct a dynamic tactile information model based on that data. The learning unit can also analyze the pressure changes of an object's tactile information and construct a dynamic model. For example, the learning unit can analyze the pressure changes of an object's tactile information and construct a dynamic tactile information model based on that data. In this way, a dynamic tactile information model can be constructed by analyzing the fluctuation patterns of an object's tactile information. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input variable data of tactile information of an object into the generating AI, allowing the generating AI to construct a dynamic model.

[0079] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can lower the learning frequency when the user is relaxed, increase it when the user is excited, and moderate it when the user is stressed. By adjusting the learning frequency according to the user's emotions, more appropriate data can be learned. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0080] The learning unit can learn not only the image and tactile information of an object, but also the usage scenario information of that object during the learning process. For example, the learning unit can learn the usage scenario information of an object simultaneously with the tactile information of that object. For example, the learning unit can measure the hardness of the surface of an object with a pressure sensor and collect that data. The learning unit can also obtain the usage scenario information of an object from a database and learn that data. Furthermore, the learning unit can learn the usage scenario information of an object simultaneously with the image of that object. For example, the learning unit can take an image of an object with a camera and learn the image data and the usage scenario information. The learning unit can also learn the relationship between the usage scenario information of an object and tactile information. For example, the learning unit can obtain the usage scenario information of an object from a database and learn the relationship between that data and tactile information. By learning the usage scenario information of an object, a wider variety of data can be obtained. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input the usage scenario data of an object into a generative AI and have the generative AI perform relationship learning.

[0081] The learning unit can extract commonalities in tactile information between different object categories during training and construct a general-purpose tactile model. For example, the learning unit can extract commonalities in hardness between different object categories and construct a general-purpose tactile model. For example, the learning unit can collect hardness data between different object categories and extract its commonalities. The learning unit can also extract commonalities in temperature between different object categories and construct a general-purpose tactile model. For example, the learning unit can collect temperature data between different object categories and extract its commonalities. The learning unit can also extract commonalities in texture between different object categories and construct a general-purpose tactile model. For example, the learning unit can collect texture data between different object categories and extract its commonalities. In this way, a general-purpose tactile model can be constructed by extracting commonalities in tactile information between different object categories. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit can input tactile data from different object categories into a generating AI and have the generating AI extract commonalities.

[0082] The acquisition unit can estimate the user's emotions and adjust the resolution of the object images it acquires based on the estimated emotions. For example, the acquisition unit can acquire low-resolution images when the user is relaxed, high-resolution images when the user is excited, and medium-resolution images when the user is stressed. By adjusting the resolution of the object images according to the user's emotions, more appropriate image data can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0083] The acquisition unit can track the movement and deformation of an object in real time during acquisition and acquire continuous image data. For example, the acquisition unit can track the movement of an object in real time and acquire continuous image data. For example, the acquisition unit can use a camera and a motion tracking algorithm to track the movement of an object in real time. The acquisition unit can also track the deformation of an object in real time and acquire continuous image data. For example, the acquisition unit can use a camera and a deformation tracking algorithm to track the deformation of an object in real time. The acquisition unit can also track the rotation of an object in real time and acquire continuous image data. For example, the acquisition unit can use a camera and a rotation tracking algorithm to track the rotation of an object in real time. In this way, continuous image data can be acquired by tracking the movement and deformation of an object in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the object's movement data into AI and have AI perform the motion analysis.

[0084] The acquisition unit can perform image processing during acquisition to remove background information from objects and enhance their contours. For example, the acquisition unit can use an image processing algorithm to remove background information from objects. The acquisition unit can also use an edge detection algorithm to enhance the contours of objects. The acquisition unit can also perform image processing to enhance the colors of objects and clarify their contours. For example, the acquisition unit can use a color enhancement algorithm to enhance the colors of objects. The acquisition unit can also perform image processing to remove shadows from objects and enhance their contours. For example, the acquisition unit can use a shadow removal algorithm to remove shadows from objects. This allows for the acquisition of clearer image data by removing background information from objects and enhancing their contours. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input image data of objects into AI and have the AI ​​perform background information removal and contour enhancement.

[0085] The acquisition unit can estimate the user's emotions and adjust the frame rate of the acquired images based on the estimated emotions. For example, the acquisition unit can acquire images with a low frame rate when the user is relaxed. It can also acquire images with a high frame rate when the user is excited. It can also acquire images with a medium frame rate when the user is stressed. By adjusting the image frame rate according to the user's emotions, more appropriate image data can be acquired. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0086] The acquisition unit can acquire not only an image of an object but also 3D model data of the object during acquisition. For example, the acquisition unit can acquire 3D model data of an object simultaneously with an image of the object. For example, the acquisition unit can take an image of an object with a camera and acquire 3D model data simultaneously with the image data. The acquisition unit can also generate 3D model data of an object based on an image of the object. For example, the acquisition unit can input the image data of an object into 3D modeling software and generate 3D model data. The acquisition unit can also acquire 3D model data of an object and integrate it with the image. For example, the acquisition unit can acquire 3D model data of an object from a database and integrate that data with the image data. By acquiring 3D model data in addition to the image of the object, more detailed data can be obtained. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the 3D model data of an object into AI and have the AI ​​perform model generation.

[0087] The acquisition unit can acquire multispectral image data by capturing images of an object with light of different wavelengths during acquisition. For example, the acquisition unit can capture images of an object with visible light and acquire multispectral image data. For example, the acquisition unit can capture images of an object with visible light and collect the data. The acquisition unit can also capture images of an object with infrared light and acquire multispectral image data. For example, the acquisition unit can capture images of an object with infrared light and collect the data. The acquisition unit can also capture images of an object with ultraviolet light and acquire multispectral image data. For example, the acquisition unit can capture images of an object with ultraviolet light and collect the data. In this way, multispectral image data can be acquired by capturing images with light of different wavelengths. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the multispectral image data of an object into AI and have AI perform data analysis.

[0088] The generation unit can estimate the user's emotions and adjust the level of detail of the haptic information it generates based on the estimated emotions. For example, the generation unit can generate low-level haptic information when the user is relaxed. It can also generate high-level haptic information when the user is excited. It can also generate medium-level haptic information when the user is stressed. By adjusting the level of detail of the haptic information according to the user's emotions, more appropriate haptic information can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0089] The generation unit can generate not only the tactile information of an object, but also its coefficient of friction and elasticity during the generation process. For example, the generation unit can generate the coefficient of friction simultaneously with the tactile information of an object. For example, when generating the tactile information of an object, the generation unit can also generate data on the coefficient of friction. The generation unit can also generate the coefficient of elasticity simultaneously with the tactile information of an object. For example, when generating the tactile information of an object, the generation unit can also generate data on the coefficient of elasticity. The generation unit can also generate both the coefficient of friction and the coefficient of elasticity simultaneously with the tactile information of an object. For example, when generating the tactile information of an object, the generation unit can also generate data on the coefficient of friction and the coefficient of elasticity. By generating the coefficient of friction and the coefficient of elasticity of an object, more detailed tactile information can be obtained. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the coefficient of friction data of an object into AI and have AI perform the data generation.

[0090] The generation unit can change the tactile information of an object over time during generation, providing dynamic tactile feedback. For example, the generation unit can change the tactile information of an object over time and provide dynamic feedback based on that data. The generation unit can also change the tactile information of an object in response to temperature changes and provide dynamic feedback. For example, the generation unit can change the tactile information of an object in response to temperature changes and provide dynamic feedback based on that data. Furthermore, the generation unit can change the tactile information of an object in response to pressure changes and provide dynamic feedback. For example, the generation unit can change the tactile information of an object in response to pressure changes and provide dynamic feedback based on that data. This allows for dynamic tactile feedback to be provided by changing the tactile information of an object over time. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input fluctuation data of the object's tactile information into the AI ​​and have the AI ​​generate dynamic feedback.

[0091] The generation unit can estimate the user's emotions and determine the priority of haptic information to generate based on the estimated user emotions. For example, the generation unit can prioritize soft haptic information when the user is relaxed. It can also prioritize hard haptic information when the user is excited. It can also prioritize warm haptic information when the user is stressed. By prioritizing haptic information according to the user's emotions, more appropriate haptic information can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0092] The generation unit can generate not only tactile information of an object but also temperature change information of an object during the generation process. For example, the generation unit can generate temperature change information simultaneously with the tactile information of an object. For example, when generating tactile information of an object, the generation unit can also generate temperature change data. The generation unit can also generate the rate of temperature change simultaneously with the tactile information of an object. For example, when generating tactile information of an object, the generation unit can also generate data on the rate of temperature change. The generation unit can also generate the range of temperature change simultaneously with the tactile information of an object. For example, when generating tactile information of an object, the generation unit can also generate data on the range of temperature change. By generating temperature change information of an object, more detailed tactile information can be obtained. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input temperature change data of an object into AI and have AI perform the data generation.

[0093] The generation unit can perform conversion processing to adapt the tactile information of an object to different tactile devices during generation. For example, the generation unit can perform conversion processing to adapt the tactile information of an object to a vibration device. For example, the generation unit can use a conversion algorithm to adapt the tactile information of an object to a vibration device. The generation unit can also perform conversion processing to adapt the tactile information of an object to a temperature device. For example, the generation unit can use a conversion algorithm to adapt the tactile information of an object to a temperature device. The generation unit can also perform conversion processing to adapt the tactile information of an object to a pressure device. For example, the generation unit can use a conversion algorithm to adapt the tactile information of an object to a pressure device. By adapting the tactile information of an object to different tactile devices, a wider variety of tactile feedback can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the tactile information of an object into AI and have AI perform the conversion to adapt to the device.

[0094] The operating unit can estimate the user's emotions and adjust the robot's operating speed based on the estimated emotions. For example, the operating unit can slow down the robot's operating speed when the user is relaxed. It can also speed up the robot's operating speed when the user is excited. It can also moderate the robot's operating speed when the user is stressed. By adjusting the robot's operating speed according to the user's emotions, more appropriate operation can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0095] The motion unit can adjust the flexibility of the robot's joints based on tactile information from an object during operation. For example, if the object is soft, the motion unit can increase the flexibility of the robot's joints. For example, if the object is soft, the motion unit can use a joint control algorithm to increase the flexibility of the robot's joints. The motion unit can also decrease the flexibility of the robot's joints if the object is hard. For example, if the object is hard, the motion unit can use a joint control algorithm to decrease the flexibility of the robot's joints. The motion unit can also set the flexibility of the robot's joints to a moderate level if the object is warm. For example, if the object is warm, the motion unit can use a joint control algorithm to set the flexibility of the robot's joints to a moderate level. By adjusting the flexibility of the robot's joints based on tactile information from an object, more appropriate operation can be achieved. Some or all of the above processing in the motion unit may be performed using AI, for example, or without AI. For example, the motion unit can input tactile information from an object into AI and have the AI ​​perform the adjustment of the joint flexibility.

[0096] The operating unit can dynamically change the robot's grip force based on tactile information from an object during operation. For example, if the object is soft, the operating unit can weaken the robot's grip force. For example, the operating unit can use a grip force control algorithm to weaken the robot's grip force when the object is soft. The operating unit can also strengthen the robot's grip force when the object is hard. For example, the operating unit can use a grip force control algorithm to strengthen the robot's grip force when the object is hard. The operating unit can also set the robot's grip force to a moderate level when the object is warm. For example, the operating unit can use a grip force control algorithm to set the robot's grip force to a moderate level when the object is warm. This allows for more appropriate operation by dynamically changing the robot's grip force based on tactile information from an object. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input tactile information from an object into the AI ​​and have the AI ​​adjust the grip force.

[0097] The robot's operation unit can estimate the user's emotions and select a robot operation pattern based on the estimated emotions. For example, the operation unit can select a slow operation pattern if the user is relaxed. It can also select a fast operation pattern if the user is excited. It can also select a stable operation pattern if the user is stressed. By selecting a robot operation pattern according to the user's emotions, more appropriate actions can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the operation unit may be performed using AI, for example, or without AI. For example, the operation unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0098] The operating unit can limit the robot's range of motion based on tactile information from an object during operation. For example, if the object is soft, the operating unit can narrow the robot's range of motion. For example, if the object is soft, the operating unit can use a range of motion control algorithm to narrow the robot's range of motion. The operating unit can also widen the robot's range of motion if the object is hard. For example, if the object is hard, the operating unit can use a range of motion control algorithm to widen the robot's range of motion. The operating unit can also moderate the robot's range of motion if the object is warm. For example, if the object is warm, the operating unit can use a range of motion control algorithm to moderate the robot's range of motion. This allows for more appropriate operation by limiting the robot's range of motion based on tactile information from an object. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input tactile information from an object into the AI ​​and have the AI ​​adjust the range of motion.

[0099] The motion unit can predict the robot's movement based on tactile information from an object during operation and create a motion plan in advance. For example, if the object is soft, the motion unit can predict the robot's movement and create a soft motion plan. For example, if the object is soft, the motion unit can use a motion prediction algorithm to predict the robot's movement and create a soft motion plan. The motion unit can also predict the robot's movement and create a hard motion plan if the object is hard. For example, if the object is hard, the motion unit can use a motion prediction algorithm to predict the robot's movement and create a hard motion plan. The motion unit can also predict the robot's movement and create a warm motion plan if the object is warm. For example, if the object is warm, the motion unit can use a motion prediction algorithm to predict the robot's movement and create a warm motion plan. This allows for more appropriate operation by predicting the robot's movement based on tactile information from an object and creating a motion plan in advance. Some or all of the above processing in the motion unit may be performed using AI, for example, or without AI. For example, the motion unit can input tactile information of an object into the AI, allowing the AI ​​to plan and execute the motion. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, learning unit, acquisition unit, generation unit, and operation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects images and tactile information of an object using the camera 42 and sensors of the smart device 14. The learning unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and learns the images and tactile information using AI. The acquisition unit acquires images of an object using the camera 42 of the smart device 14. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates tactile information based on the learned model. The operation unit is implemented, for example, in the control unit 46A of the smart device 14, and controls the robot's movements based on the generated tactile information. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, learning unit, acquisition unit, generation unit, and operation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects images and tactile information of objects using the camera 42 and sensors of the smart glasses 214. The learning unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and learns the images and tactile information using AI. The acquisition unit acquires images of objects using the camera 42 of the smart glasses 214. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates tactile information based on the learned model. The operation unit is implemented, for example, in the control unit 46A of the smart glasses 214, and controls the robot's movements based on the generated tactile information. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, learning unit, acquisition unit, generation unit, and operation unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects images and tactile information of an object using the camera 42 and sensors of the headset terminal 314. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and learns the images and tactile information using AI. The acquisition unit acquires images of an object using the camera 42 of the headset terminal 314. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates tactile information based on the learned model. The operation unit is implemented, for example, by the control unit 46A of the headset terminal 314, and controls the robot's movements based on the generated tactile information. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, learning unit, acquisition unit, generation unit, and operation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects images and tactile information of objects using the camera 42 and sensors of the robot 414. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and learns the images and tactile information using AI. The acquisition unit acquires images of objects using the camera 42 of the robot 414. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates tactile information based on the learned model. The operation unit is implemented, for example, by the control unit 46A of the robot 414, and controls the robot's movements based on the generated tactile information.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can collect chemical composition information of an object simultaneously with collecting image and tactile information. For example, the data collection unit can measure the surface hardness of an object using a pressure sensor and collect the data. It can also analyze the chemical composition of an object using a spectrometer and collect the data. Furthermore, the data collection unit can measure the temperature of an object using a temperature sensor and collect the data. By simultaneously collecting chemical composition information, more detailed data can be obtained. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the chemical composition data of an object into an AI and have the AI ​​perform the analysis of the components.

[0102] The learning unit can learn information about the object's environment when learning its image and tactile information. For example, the learning unit can measure the hardness of the object's surface using a pressure sensor and collect that data. It can also obtain information about the object's environment from a database and learn that data. Furthermore, the learning unit can learn information about the environment simultaneously with the object's image. By learning information about the object's environment, it can obtain a wider variety of data. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the object's environment data into a generative AI and have the generative AI perform relevance learning.

[0103] The acquisition unit can acquire continuous image data by tracking the dynamic changes of an object in real time when acquiring an image of the object. For example, the acquisition unit can use a camera and a motion tracking algorithm to track the movement of an object in real time. The acquisition unit can also track the deformation of an object in real time and acquire continuous image data. Furthermore, the acquisition unit can track the rotation of an object in real time and acquire continuous image data. In this way, continuous image data can be acquired by tracking the movement and deformation of an object in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the object's motion data into AI and have AI perform motion analysis.

[0104] The generation unit can generate the friction coefficient and elastic modulus of an object when generating tactile information about that object. For example, the generation unit can generate friction coefficient data when generating tactile information about an object. Furthermore, the generation unit can generate the elastic modulus simultaneously with the tactile information of an object. In addition, the generation unit can generate both the friction coefficient and elastic modulus simultaneously with the tactile information of an object. This allows for the generation of more detailed tactile information by also generating the friction coefficient and elastic modulus of an object. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input friction coefficient data for an object into an AI and have the AI ​​generate the data.

[0105] The motion unit can predict the robot's movements based on tactile information from an object and create a motion plan in advance. For example, if the object is soft, the motion unit can use a motion prediction algorithm to predict the robot's movements and create a soft motion plan. The motion unit can also predict the robot's movements and create a hard motion plan if the object is hard. Furthermore, if the object is warm, the motion unit can predict the robot's movements and create a warm motion plan. This allows for more appropriate movements by predicting the robot's movements based on tactile information from an object and creating a motion plan in advance. Some or all of the above processing in the motion unit may be performed using AI, for example, or without AI. For example, the motion unit can input tactile information from an object into an AI and have the AI ​​create the motion plan.

[0106] The data collection unit can estimate the user's emotions and select objects to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit can prioritize collecting soft objects. If the user is excited, the data collection unit can prioritize collecting hard objects. Furthermore, if the user is stressed, the data collection unit can prioritize collecting warm objects. By selecting objects to collect according to the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0107] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can prioritize learning data on soft objects. If the user is excited, the learning unit can prioritize learning data on hard objects. Furthermore, if the user is stressed, the learning unit can prioritize learning data on warm objects. This allows for the selection of training data according to the user's emotions, enabling the learning of more appropriate data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0108] The acquisition unit can estimate the user's emotions and adjust the resolution of the object images it acquires based on the estimated user emotions. For example, the acquisition unit can acquire low-resolution images when the user is relaxed. It can also acquire high-resolution images when the user is excited. Furthermore, it can acquire medium-resolution images when the user is stressed. By adjusting the resolution of the object images according to the user's emotions, more appropriate image data can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0109] The generation unit can estimate the user's emotions and adjust the level of detail of the haptic information it generates based on the estimated user emotions. For example, the generation unit can generate low-level haptic information when the user is relaxed. It can also generate high-level haptic information when the user is excited. Furthermore, it can generate medium-level haptic information when the user is stressed. By adjusting the level of detail of the haptic information according to the user's emotions, more appropriate haptic information can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0110] The operating unit can estimate the user's emotions and adjust the robot's operating speed based on the estimated emotions. For example, the operating unit can slow down the robot's operating speed when the user is relaxed. It can also speed up the robot's operating speed when the user is excited. Furthermore, it can moderate the robot's operating speed when the user is stressed. By adjusting the robot's operating speed according to the user's emotions, more appropriate actions can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects images and tactile information of the object. For example, the collection unit acquires tactile information such as the hardness, temperature, and texture of the object's surface using sensors, and simultaneously captures images of the object with a camera. For example, the collection unit measures the hardness of the object using a pressure sensor and measures the temperature of the object using a temperature sensor. The collection unit can also capture the texture of the object's surface using a camera. Step 2: The learning unit learns from the images and tactile information collected by the collection unit. The learning unit links images and tactile information, for example, using AI. The AI ​​can learn the relationship between images and tactile information, for example, using deep learning technology. Step 3: The acquisition unit acquires an image of an object based on the data learned by the learning unit. The acquisition unit can, for example, have a robot take an image of an object using a camera. The acquisition unit can, for example, acquire a detailed image of an object using a high-resolution camera. Step 4: The generation unit generates tactile information based on the images acquired by the acquisition unit. The generation unit generates tactile information of an object based on, for example, the model built in the learning phase. The generation unit can generate tactile information of an object with high accuracy using, for example, AI. Step 5: The operating unit performs robotic actions based on the tactile information generated by the generating unit. For example, the operating unit controls the robot's actions based on the generated tactile information. For example, the operating unit can adjust the movement of the robot's joints and perform actions in accordance with the tactile information of an object.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects images and tactile information of an object, A learning unit that learns images and tactile information collected by the aforementioned collection unit, An acquisition unit that acquires an image of an object based on the data learned by the learning unit, A generation unit generates tactile information based on the image acquired by the acquisition unit, An operating unit that performs robotic actions based on tactile information generated by the generation unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Sensors acquire tactile information about the hardness, temperature, and texture of an object's surface, while simultaneously capturing an image of the object with a camera. The system according to feature 1.

3. The aforementioned learning unit, AI links images with tactile information. The system according to feature 1.

4. The acquisition unit is, The robot uses a camera to take an image of an object. The system according to feature 1.

5. The generating unit is Based on the model built during the learning phase, tactile information of an object is generated. The system according to feature 1.

6. The aforementioned operating unit is The robot performs actions when handling objects based on the generated tactile information. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and selects objects to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is In addition to the hardness, temperature, and texture of the object's surface, the vibrational and acoustic properties of the object are also collected. The system according to feature 1.

9. The aforementioned collection unit is During collection, different sensors are used depending on the shape and dimensions of the object. The system according to feature 1.

Images