Robot Instruction Command Generation from Recipe Image and Text Data
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Solution Overview
Problem
Current technologies require manual programming of robots for cooking tasks, which is inconvenient and inefficient, especially when using instruction data like recipes that are intended for human viewing.
Innovation Solution
A data processing device generates instruction commands for robots with arms based on image and text data from recipe instructions, allowing the robot to autonomously perform cooking tasks without the need for manual programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual programming is used to control robot arms for cooking tasks, then the robot can execute precise operations, but the complexity of programming increases significantly
Solution Approach 1:
The patent captures images of the cooking process and uses image recognition to identify objects, states, and actions. Instead of manually programming each step, the system creates a digital representation (copy) of the cooking process through image data, which is then processed to generate robot control commands. This reduces programming complexity while maintaining execution precision through visual feedback.
Solution Approach 2:
The patent replaces manual mechanical programming with an automated vision-based system. Image capturing devices and image recognition algorithms substitute for manual programming efforts, automatically translating visual information into robot control commands. This substitution reduces the complexity of programming while maintaining reliable execution through automated image-based control.
2Manufacturing precision
If detailed programming is provided for robot cooking tasks, then the robot can perform operations accurately, but the time and effort required for preparation increases
Solution Approach 1:
The patent performs preliminary image capturing and recognition to identify cooking objects, states, and actions before generating robot control commands. By pre-processing visual information and creating a structured representation of the cooking process, the system reduces the time required for programming while ensuring accurate operation execution through pre-identified target objects and actions.
Solution Approach 2:
The system creates a visual copy of the cooking process through image capture and recognition, which automatically encodes the necessary operational information. This copying approach eliminates the need for time-consuming manual programming while preserving operation accuracy through automated extraction of cooking steps from visual data.
3Ease of operation
If instruction data is designed for human viewing, then it is easy to understand and follow, but it requires conversion to robot control commands
Solution Approach 1:
The patent uses image recognition and natural language processing to automatically convert human-readable instruction data into robot control commands. The system substitutes manual conversion efforts with automated algorithms that interpret visual and textual information from recipes, generating appropriate control commands without requiring complex manual programming while maintaining ease of instruction understanding.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates between human-readable instruction data and robot control commands. This intermediary system uses image recognition to identify objects and actions in recipe images, then converts this information into structured control commands, bridging the gap between human-understandable instructions and machine-executable commands without exposing users to complexity.
Data Source
AI summary
A data processing device includes a command generation unit configured to generate an instruction command for giving an instruction of one or more operations to be executed during a process by a robot provided with at least one arm, wherein the instruction command is generated on a basis of instruction data including image data obtained by capturing one or more images of situations during or after the process, and text data indicating at least one of an object to be utilized in the process or an operation to be executed during the process.