Robot Control Code Generation From Natural Language Instructions
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Solution Overview
Problem
Existing robot operation techniques using machine learning are limited in their ability to efficiently convert simple user instructions into executable programming code for controlling robots in real-world environments, often requiring complex and error-prone manual programming.
Innovation Solution
A robot system utilizing a conversion language model generated by machine learning to convert user input sequence data, such as natural language or images, into programming code for robot operations, with additional data processing to ensure executable and accurate robot control, including verification and correction mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual programming is used to control robots in real-world environments, then operational accuracy can be maintained, but the complexity and error-proneness of programming increases significantly
Solution Approach 1:
The patent introduces an intermediate processing system that translates natural language instructions into robot control commands. This intermediary layer handles the complexity of programming internally while presenting a simple interface to users, thereby maintaining operational accuracy without requiring users to deal with complex programming directly.
Solution Approach 2:
The patent replaces traditional manual programming mechanisms with an automated natural language processing system. Instead of requiring users to manually write and debug code, the system automatically converts spoken or written instructions into executable robot commands, reducing programming complexity while maintaining reliability through verification mechanisms.
2Ease of operation
If simple user instructions are accepted as input, then ease of operation improves, but the ability to generate accurate executable code for real-world constraints deteriorates
Solution Approach 1:
The patent performs preliminary processing of user instructions by automatically generating candidate code, verifying it against real-world constraints, and correcting errors before execution. This preliminary verification and correction process ensures that simple user instructions are transformed into accurate executable code that accounts for environmental constraints.
Solution Approach 2:
The system implements feedback mechanisms where generated code is verified against real-world constraints and user intentions. If the code does not meet accuracy requirements or constraints, the system automatically adjusts and regenerates the code, ensuring that simple instructions ultimately produce accurate executable programs.
3Productivity
If conversion language model is used to translate user input into programming code, then productivity increases, but the need for verification and correction mechanisms adds system complexity
Solution Approach 1:
The patent merges the conversion language model, verification module, and correction mechanisms into an integrated system. By combining these functions into a unified processing pipeline, the system maintains high productivity while managing complexity through coordinated operation of interconnected components rather than separate standalone systems.
Solution Approach 2:
The system performs self-verification and self-correction of generated code automatically without requiring external intervention. The verification and correction mechanisms operate autonomously to ensure code quality, reducing the need for complex external validation systems while maintaining productivity.
Data Source
AI summary
A robot system includes circuitry configured to: receive input sequence data representing an operation of a robot placed in a real space; input the input sequence data into a conversion language model generated by machine learning to convert the input sequence data into output sequence data, wherein the output sequence data is programming code; and control the robot to perform the operation represented by the input sequence data, based on the output sequence data.