Robot Control via Language Model Sequence Conversion
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Solution Overview
Problem
Existing methods for operating robots are inefficient and require significant improvements in terms of complexity and accuracy.
Innovation Solution
A robot system utilizing a conversion language model generated by machine learning to convert input sequence data into output sequence data, enabling simpler instructions for robot operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional robot operation methods are used, then the robot can perform operations, but the instruction complexity and operational difficulty are high
Solution Approach 1:
The patent introduces a language model as an intermediary between the user and the robot control system. The language model converts natural language instructions into executable robot commands, eliminating the need for users to directly write complex programming code. This mediator handles the complexity transformation, allowing simple textual input to produce sophisticated robot operations.
Solution Approach 2:
The patent replaces traditional mechanical programming interfaces (buttons, dials, code editors) with a linguistic interface. Instead of manipulating physical controls or writing structured code, users simply type natural language descriptions of desired robot actions. The system translates these linguistic inputs into the appropriate control signals, substituting the mechanical interaction paradigm with a more intuitive linguistic one.
2Productivity
If traditional programming methods are used for robot control, then precise operations can be achieved, but the time and effort required for programming are significant
Solution Approach 1:
The language model performs self-service by automatically generating the complete robot control program from a single natural language description. Instead of requiring programmers to manually write, debug, and optimize code line-by-line, the system autonomously translates the high-level intent into executable instructions, significantly reducing both programming time and human effort.
Solution Approach 2:
The system performs preliminary analysis and planning of the robot operation sequence automatically. When receiving a natural language instruction, the language model pre-processes the request, determines the necessary action sequence, selects appropriate commands, and prepares the complete program before execution. This preliminary automated processing eliminates the time-consuming manual programming steps.
Data Source
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AI summary
A robot system comprises: a reception unit configured to receive input sequence data representing an operation of a robot placed in a real space; a conversion unit configured to input the input sequence data into a conversion language model generated by machine learning to convert the input sequence data into output sequence data; and a robot control unit configured to control the robot to perform the operation, based on the output sequence data.