1650 results about "Humanoid robot nao" patented technology

A humanoid robot leg that may include a shank, an ankle that includes an ankle joint that is configured to perform yaw and pitch rotations, a knee joint, multiple ankle related (AR) rotational motors that are in mechanical communication, via multiple AR transmission mechanisms, with the ankle joint, and a knee related (KR) rotational motor that is in mechanical communication, via a KR transmission mechanism, with the knee joint. The multiple AR rotational motors and the KR rotational motor are positioned above the knee joint.

A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a frontal shell having a rear edge, a rear shell having a frontal edge, and an electronics assembly. The electronics assembly includes various components and devices used in the operation of the humanoid robot.

A head and neck assembly for a humanoid robot, including a head portion having an exterior surface defining an overall shape resembling a human head; a neck portion extending from the head portion; a head housing assembly enclosing the head portion and neck portion; an electronics assembly contained within the head housing assembly; and a head actuator assembly configured to move the head portion relative to a torso of the humanoid robot.

The invention relates to a humanoid robot double-arm body operation method based on reinforcement learning, belongs to the field of robot cooperative control, and is characterized in that a strategy of trajectory block prediction and time integration fusion is used for double-arm cooperative control, and continuity and stability of double-arm operation are improved; in reinforcement learning control, a three-dimensional pose track generation and correction module is introduced, condition generation and denoising correction of a three-dimensional space are carried out on a track block layer, and geometric consistency and naturalness of a generated track are guaranteed; the invention further provides a reinforcement learning optimization framework and a simulation-reality migration process, and through system integration of reward, value guidance and migration processes, strategy deployability and safety are guaranteed; compared with the prior art, the method has the advantages that the naturalness, the collaboration and the success rate of double-arm operation can be remarkably improved, the generalization ability is high, and the good simulation-to-reality migration ability is achieved.

A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a frontal shell having a rear edge, a rear shell having a frontal edge, and an electronics assembly. The electronics assembly includes various components and devices used in the operation of the humanoid robot.

The invention relates to the technical field of robot cooperative control, in particular to a humanoid robot body intelligent cooperative control system based on multi-modal sensing fusion, and the system comprises a multi-modal sensing fusion module which recognizes a target boundary position, analyzes a pressure change, and combines with a posture to extract audio features to generate an environment sensing graph; the dynamic time sequence adjustment module optimizes an action rhythm adjustment detail generation execution plan, the cross-modal behavior correction module corrects an offset optimization track generation coordination sequence, the task priority distribution module analyzes task distribution to generate an execution list, and the time sequence conflict correction module optimizes a path adjustment conflict generation coordination path. According to the method, an environment perception graph is constructed through matching and fusion of multi-source perception data, the action sequence and interval are dynamically adjusted to optimize an execution chain, trajectory offset is corrected to improve action precision, nearest response and load balancing are achieved through real-time task allocation, conflict blocking is reduced through path rearrangement and time sequence coordination, and a perception decision execution closed loop is formed; and the identification precision and the cooperation efficiency are improved.

The present disclosure provides a system for generating motor control commands for a humanoid robot, comprising an alpha model with over 1 billion parameters that processes visual observations and language instructions at a first frequency to generate contextual embeddings, and a beta model operating at a higher second frequency. The beta model includes an embodiment-specific state encoder projecting robot state information into a shared embedding space, a diffusion transformer module generating denoised action sequences through iterative flow-matching that cross-attends to the alpha model's contextual embeddings, and an embodiment-specific action decoder converting denoised sequences into motor control commands. The beta model generates action chunks comprising future action sequences over a predetermined time horizon in a single inference step, with the complete system having less than 5 billion parameters.

The present disclosure provides a humanoid robot comprising a torso having an alpha model deployed on a first GPU, and wherein said alpha model includes a first number of parameters and is configured to receive a natural language command from a human and generate processed data, a beta model deployed on a second GPU, and wherein said beta model includes a second number of parameters and is configured to receive the processed data from the alpha model and provide output data used to control an extent of the left wrist, and wherein the first number of parameters is larger than the second number of parameters, and a unified training framework is used to jointly train the alpha model and the beta model.

A robotic torso for a humanoid robot includes a first torso member having an axial axis aligned with a reference axis and a second torso member having at least one mounting portion for at least one humanoid component. The robotic torso includes a series of actuators arranged in a column between the first torso member and the second torso member and coupling the first torso member to the second torso member. Each of the actuators has a rotatable member defining a rotational axis. The rotational axes of each adjacent pair of actuators in the series of actuators are orthogonal to each other.

The invention discloses a full-size integrated humanoid robot which comprises a trunk mechanism, arm mechanisms arranged on the left side and the right side of the upper end of the trunk mechanism, a hip mechanism arranged at the lower end of the trunk mechanism and leg mechanisms arranged at the left end and the right end of the hip mechanism. The driving modules of the arm mechanisms and the leg mechanisms are mounted in the inner cavities of the corresponding curved-surface metal shells through bolts respectively; the hip mechanism comprises a hip assembly and hip curved surface shells installed on the front side and the rear side of the hip assembly. The hip assembly comprises a hip base connected with an output shaft of a trunk driving module of the trunk mechanism. Hip driving modules are symmetrically arranged at the left end and the right end of an inner cavity of the hip base; a control circuit board of the hip driving module is arranged on the top of the hip base through a fixing plate. On the basis of replacing a traditional shell and a skeleton with the curved metal shell, the size of the hip mechanism is reduced, the assembly steps are simplified, the light weight of the humanoid robot is achieved, and the requirement for structural strength is met.

The invention discloses a petrochemical explosion-proof humanoid robot trajectory planning method, device and equipment, and the method comprises the steps: collecting petrochemical environment gas concentration data, robot joint motion parameters and target position information, carrying out the dangerous region calibration of the gas concentration data, generating a forbidden map, and determining safety nodes and joint motion ranges; constructing an initial path skeleton based on target position information and safety node distribution, identifying a local dangerous section and a major dangerous section, and generating a motion constraint condition and a poison avoidance path; generating a motion primitive library based on the motion constraint condition and the joint motion range, performing stability filtering to generate stable motion primitives, and forming an advancing scheme set; acquiring a joint load peak value to construct a dynamic envelope, and judging execution fitness to form an execution probability field; and constructing a three-dimensional decision space based on the poison avoidance path, the advancing scheme set and the execution probability field, searching an optimal convergence point to generate a final planning trajectory, and providing a safe and efficient trajectory planning scheme for the petrochemical explosion-proof humanoid robot.

The present disclosure provides a method for generating annotation data for robotic training using a hierarchical transformer-based model with multiple layers. The transformer-based model includes Alpha models generating low-level control outputs and Beta models generating high-level control outputs. The method receives multimodal input data comprising visual sensor data and natural language instructions, processes this data through the hierarchical transformer-based model to generate annotations at different abstraction levels, wherein Beta models create semantic annotations describing task objectives and Alpha models generate motor command annotations specifying robotic actions, and stores these annotations with the input data to create annotated training data for robotic control systems.

The invention discloses a humanoid robot motion control method based on gait planning and reinforcement learning, and belongs to the technical field of humanoid robot motion control. According to the method, the problems of low convergence speed in a training process and poor stability and reliability of a trained model of an existing reinforcement learning method are solved. According to the method, a reinforcement learning model runs in parallel under multiple environment instances, in the running process, a target foothold is generated in real time based on feedback state information, the generated reference foothold and a mass center track serve as an action reference or reward target of a controller, and a continuous and dynamically-adjusted foot end track is constructed in combination with gait phase information. And after the reinforcement learning model is trained by utilizing all the collected trajectory data, motion control can be performed on the humanoid robot through the trained reinforcement learning model, so that the robot realizes stable walking similar to human beings under the condition of not depending on a predefined template. The method can be applied to motion control of the humanoid robot.

The embodiment of the invention relates to the technical field of robots, and discloses a demonstration method and system for a humanoid robot based on vision, and the method comprises the steps: obtaining a human body demonstration image of a demonstrator in a current scene through a camera assembly; recognizing and analyzing the continuous frames of human body teaching images to obtain human body key point coordinates in each moving stage, and calculating to obtain a joint motion state of each control joint of the corresponding robot; and generating a simulation control instruction of each joint of the humanoid robot according to the joint motion state of each control joint, and sending the simulation control instruction to the corresponding humanoid robot for action simulation. According to the demonstration method of the humanoid robot based on the vision, the human body demonstration image of the demonstrator is obtained through the camera assembly, the demonstrator does not need to wear complex sensor equipment, and the demonstration cost and the operation difficulty are reduced.

This patent discloses a computer system for humanoid robot control system design and implementation, featuring a digital humanoid robot with dynamical models and a set of single-input-single-output (SISO) and multi-input-multi-output (MIMO) controllers. The system comprises a main software program, a generative Al humanoid robot intelligence engine, a robot motion path planner module, and a control system simulation engine. It enables efficient design, testing, validation, and implementation of robot control systems, significantly reducing time to market. The system supports seamless upgrades to accommodate new designs and components, enhancing applications in industrial automation, healthcare, public safety, and more, aligning with the goals of the 4th Industrial Revolution.

Various advanced wiring assemblies for a humanoid robot are disclosed. The wiring assembly includes a first actuator printed circuit board (PCB) positioned near a first side of a first actuator and including a first PCB terminal. A second side of the first actuator includes: an output, an actuator cover coupled to the output and having a wire bundle opening formed therein, and an actuator opening formed through an extent of the second side. The wiring assembly includes a wire bundle having: a first end connector coupled to the first PCB terminal, a second end connector coupled to the second PCB terminal, and a plurality of wires that extend between the first end connector and the second end connector, and wherein said plurality of wires extends through the actuator opening of the second side of the first actuator and the wire bundle opening formed in the actuator cover.

The present disclosure provides a method for calibrating a humanoid robot, comprising obtaining a humanoid robot with original kinematic biasing values, controlling the humanoid robot through predetermined poses, capturing image data of body parts using vision sensors mounted on the humanoid robot while moving through the poses, determining revised kinematic biasing values by processing the image data using a bipedal spatial perception model trained using synthetic image data containing keypoints, and replacing the original kinematic biasing values with the revised kinematic biasing values. The bipedal spatial perception model processes captured image data to generate observed keypoint locations on robot components, which are compared with kinematic-based locations from joint encoder measurements to minimize discrepancies through optimization algorithms.

The invention belongs to the technical field of robot navigation, and particularly relates to a humanoid robot navigation method based on visual semantic segmentation and radar obstacle detection, which comprises the following steps: synchronously acquiring an RGB image and a depth image of a current environment of a humanoid robot by using a visual sensor, and acquiring point cloud data by using a laser radar; performing semantic segmentation on the preprocessed RGB image; radar point cloud obstacle detection; fusing the semantic segmentation map and the laser point cloud map, introducing a Bayesian decision to judge whether the laser point cloud map is passable or not, and then calculating a fusion cost value to obtain a fusion cost map; adopting an RRT * / TEB algorithm to output an optimal path; using a nonlinear optimization solver to generate a foothold sequence accurate to each step; according to the method, a semantic-geometric two-dimensional navigation cost model is constructed; bayesian reasoning is deeply bound with a navigation scene, so that the navigation adaptability of an unstructured environment is improved; the navigation method is suitable for the humanoid robot, and is low in energy consumption, low in navigation deviation and high in safety.

The invention provides a humanoid robot multi-modal visual calibration system and an operation method thereof, and the system is characterized in that a closed-loop control system integrating synchronous collection, data alignment, feature fusion, online correction, hand-eye relationship calculation and quality evaluation feedback is constructed; according to the invention, efficient collaboration of multi-modal visual sensing information and automatic maintenance and optimization of a hand-eye relationship are realized, so that the problem of precision attenuation caused by misalignment of a relationship between sensors in the prior art is effectively solved; and the accuracy, consistency and long-term reliability of the humanoid robot in sensing and operation based on multi-modal vision in a complex and changeable environment are remarkably improved.

The invention discloses a humanoid robot reinforcement learning gait control method and system, and belongs to the technical field of robot control. Comprising the following steps: constructing a model prediction control optimization problem based on a linear inverted pendulum model, and generating a robot mass center track and a foothold sequence; collecting state-action pair data of model prediction control, and training a neural network model by adopting supervised learning; designing a reinforcement learning strategy network, taking the output of the neural network model as a physical guidance reference, calculating a training reward based on the constructed composite reward function, training the strategy network in a high-concurrency simulation environment by adopting a near-end strategy optimization algorithm, and outputting a joint action instruction; and verifying the trained strategy and the joint action instruction output by the trained strategy in a plurality of simulation environments, and deploying the trained strategy and the joint action instruction to a real machine for dynamic gait control. According to the method, the stability and adaptability of gait control of the humanoid robot are improved through a method of combining model predictive control and reinforcement learning.

The invention relates to an all-humanoid robot motion redirection method and device based on motion capture equipment, and the method comprises the steps: converting a human body posture quaternion collected by the motion capture equipment into a rotation matrix, and achieving the precise mapping from a Cartesian space to a robot joint space in combination with an Euler angle decomposition strategy; a quaternion-disassembled joint angle is used as an initial solution, various physical constraints are introduced in real time according to the actual supporting state of the robot, and the lower limb joint angle is corrected online by constructing and solving an inverse kinematics problem with various constraints, so that the robot reproduces the action of an operator, and the joint angle of the lower limb can be corrected online. And the requirements of joint limiting, self-collision avoidance, dynamic balance and the like are strictly met, so that the accuracy of motion simulation and the stability of the system are effectively unified.

The invention discloses a real-time whole-body remote operation control system for a humanoid robot, and the system comprises a motion instruction generation subsystem which is located at an operation end and is used for receiving and analyzing input instructions of various modes, and carrying out the unified conversion and complementation of the input instructions into a whole-body joint motion instruction which is suitable for a skeleton structure of a target humanoid robot; the body motion control subsystem is located at the robot end and used for receiving the whole-body motion instruction, adaptively generating a whole-body motion coordination control signal capable of ensuring the stability of the robot in combination with the state of the robot and environment information, and driving and executing the whole-body motion coordination control signal; through cooperation of the two subsystems, the system provides a dual guarantee path for realizing whole-body teleoperation: a complete whole-body instruction can be generated based on the instruction generation subsystem, and whole-body motion can be adaptively generated by the body motion control system according to a received local instruction; and the robustness, the flexibility and the reliability of the system under different conditions are ensured.

The invention provides a humanoid robot hand-eye coordination intelligent calibration control system and a humanoid robot hand-eye coordination intelligent calibration control method, and the humanoid robot hand-eye coordination intelligent calibration control system integrates high-precision contour extraction, visual feedback closed-loop control, multi-sensor fusion and dynamic path planning. The robot hand-eye calibration method has the beneficial effects that the calibration efficiency and precision can be remarkably improved, the problem of calibration parameter drift caused by system time-varying characteristics and environmental interference is effectively solved through continuous tracking of a target contour and real-time online compensation of pose deviation, and the calibration accuracy is improved. Therefore, the reliability and the absolute positioning precision of the robot in long-term complex operation tasks are guaranteed, and the self-adaptive capability of a robot system is enhanced.

The invention discloses a method for completing precise control of a dexterous hand of a humanoid robot by using a visual identification technology, and relates to the technical field of humanoid robot joint control, and the method comprises the following steps: S1, constructing a visual perception architecture: constructing a layered visual architecture through cooperation of a sensor to meet the perception demand of covering global to local, and meanwhile, the precision requirements of different operation scenes are met. According to the method for completing precise control over the dexterous hand of the humanoid robot through the visual recognition technology, global coverage and local fineness can be considered through the arranged layered visual perception architecture, full-link perception from operation space positioning to target grabbing point fine recognition and then to fingertip action high-speed monitoring is achieved, and the accuracy of the control over the dexterous hand of the humanoid robot is improved. The global space requirement of robot moving operation is met, the submillimeter-level grabbing precision of the dexterous hand is guaranteed, and through the design of closed-loop control and multi-mode fusion, self-adaptive high-precision grabbing of the humanoid robot is achieved, and real-time monitoring of fingertip deformation and self-adaptive adjustment of gripping force are achieved.

The invention relates to the technical field of humanoid robot control. The invention provides a humanoid robot bilateral force feedback teleoperation method of a force compensation mechanism. According to the method, a 5G private network is used for deployment, the characteristics of low delay, high reliability and large bandwidth of the 5G private network are utilized, real-time interaction of high-dimensional haptic data in a remote and cross-domain environment is effectively supported, cross-domain teleoperation can be achieved, high-fidelity feedback transmission of environment contact force is effectively achieved, and the real-time interaction of the high-dimensional haptic data in the remote and cross-domain environment is achieved. The precision, the operation efficiency and the overall safety of remote operation of the humanoid robot in complex industrial scenes such as nuclear power maintenance, precise assembly and high-risk environment operation are remarkably improved, the gap between traditional automation and low-efficiency remote operation is filled up, and reliable technical support is provided for advanced industrial application needing man-machine cooperation fine force control.

The invention discloses a humanoid robot walking and running unified control method and system based on a biological rhythm, and belongs to the technical field of humanoid robots. Capturing human walking and running action data, and extracting rhythm features from frequency and phase time domains; constructing a rhythm generator based on the rhythm features extracted in the step 1; according to a strategy gradient method based on a constraint reinforcement learning algorithm, a walking and running unified control strategy is constructed, rhythm information generated based on a rhythm generator is adopted in the walking and running unified control strategy, then action-critic is used for training, a bionic motion control system Walk2Run under speed driving is jointly constructed, natural gait transition and frequency adjustment are achieved, and the walking and running unified control strategy is obtained. And meanwhile, the physical capacity limitation of the robot is met, and motion fluency and energy efficiency are improved. The problems that an existing humanoid robot does not achieve energy optimization of human walking during walking, and mobility of an existing data set has high requirements for the motion ability of the robot and is poor in generalization ability are solved.

The invention relates to the technical field of robots, and discloses an electro-hydraulic hybrid driving and driven humanoid robot lower limb and waist assembly which serves as a connection foundation of lower limbs and a robot trunk. The near ends of the thigh assemblies are connected with the waist assembly through hip joints; the near end of the shank assembly is connected with the far end of the thigh assembly through a knee joint; the foot component is connected with the far end of the shank component through an ankle joint; the hip joint is provided with three rotational degrees of freedom, the knee joint is provided with one rotational degree of freedom, and the ankle joint is provided with two rotational degrees of freedom; the rotational degree of freedom of each joint in a sagittal plane is driven by an active hydraulic actuator and a passive hydraulic actuator; the rotational degree of freedom of each joint in a horizontal plane and a coronal plane is driven by a motor joint module; the advantages of electric drive and hydraulic drive are brought into full play, and the problems of high power consumption in the negative work stage, complex traditional hydraulic drive pipelines and the like are solved by adopting an active and passive combined distributed hydraulic drive technology.

A humanoid robot based on AR / VR relates to the technical field of robots and structurally comprises a humanoid robot body, an AR / VR hybrid interaction terminal and a low-delay communication network. The robot body comprises a chassis, a double-arm mechanism, a main control system, a joint driving system, a multi-mode sensor assembly, a visual system, an edge calculation unit and a body intelligent decision module. And the hybrid interaction terminal identifies a user instruction to control the robot body and performs somatosensory feedback on the user. The control method comprises the following steps: 1, starting the system and establishing communication connection; 2, selecting an operation mode and inputting an instruction; 3, identifying a user instruction, generating an execution instruction and transmitting the execution instruction to the main control system; 4, calculating a risk value; 5, switching modes according to the risk value; and 6, the mixed interaction terminal receives the returned data and carries out somatosensory feedback. According to the invention, the technical problems of low efficiency, lack of autonomous judgment of environmental danger and adoption of emergency means and poor safety caused by single interaction mode of the existing operation mode can be solved.

The invention belongs to the field of robot motion control, and particularly relates to a humanoid robot multi-terrain gait control method and system fused with visual perception. The method comprises the following steps: acquiring multi-modal sensing data of the humanoid robot, and preprocessing the multi-modal sensing data; the preprocessed multi-modal sensing data are input into a pre-trained world model, and the world model updates and outputs a potential state at the current moment based on a historical recursive state, a historical random posteriori state and an action sequence in a historical updating interval; inputting the potential state at the current moment into a pre-trained strategy network, and outputting an action at the current moment; and based on the action at the current moment, the driving torque of each joint is calculated through a PD controller, and the humanoid robot is driven to move. According to the method, a world model structure is introduced, so that the robot can realize more stable and more efficient gait control and terrain adaptation of the humanoid robot under the condition that the robot only depends on perception information which can be acquired by the robot.

A multi-degree-of-freedom bionic arm of a humanoid robot belongs to the technical field of robots, and seven degrees of freedom of the robot arm are formed by a shoulder swing arm rotating mechanism, a shoulder left-right swing arm rotating mechanism, a big arm axial rotating mechanism, an elbow rotating mechanism, a small arm axial rotating mechanism, a wrist rotating mechanism and a wrist inner drive rotating mechanism. Holes and grooves are formed in the elbow part for laying cables, the cables are not exposed, and the shell size of the elbow joint is reduced. And the cable does not run about when the elbow moves in a large range, so that interference with other parts is avoided. A connecting rod mechanism composed of a first output disc, two connecting rods and a second output disc is arranged on the side face, the load inertia of a forearm joint, a whole arm force arm and the tail end is reduced, and the arm load capacity and motion performance are improved; the wrist drives the front-back and left-right freedom degree axes of the hand to coincide at one point, and the flexibility of the wrist is improved. Due to seven degrees of freedom, the robot is more bionic, the degrees of freedom are also on the same straight line after the arms of a person are straightened laterally, and the movement offset is small.