Robot Task Planning With LLM Feedback for Fault Resolution
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Solution Overview
Problem
Existing robot systems lack efficient automation of tasks through integrated large language models (LLMs) for processes like task planning, motion planning, and human interaction, limiting their ability to adapt and perform complex tasks autonomously.
Innovation Solution
Integrating a large language model (LLM) to process sensor data, generate natural language descriptions, and interact with a robot system to automate task planning, motion planning, and logic reasoning, allowing for fault detection and resolution in task plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional robot control systems are used, then device complexity is reduced, but task planning capability and adaptability are insufficient
Solution Approach 1:
A natural language processing intermediary layer is introduced between the robot's sensor data and the LLM, converting sensor inputs into natural language descriptions that the LLM can process. This intermediary enables complex task planning capabilities without requiring direct integration of complex planning algorithms into the robot's core control system.
Solution Approach 2:
Traditional mechanical task planning systems based on predefined rules and algorithms are replaced with an LLM-based cognitive system. The LLM processes natural language descriptions of the environment and generates task plans through language understanding rather than mechanical rule execution, significantly improving adaptability.
2Productivity
If LLM is integrated for automated task planning, then productivity is improved, but reliability of task execution may deteriorate due to potential faults in generated plans
Solution Approach 1:
A feedback mechanism is implemented where the robot executes task plans generated by the LLM and reports outcomes back to the system. When execution failures or faults are detected, the system uses natural language to communicate these issues to the LLM, which then generates corrected task plans. This closed-loop feedback ensures continuous improvement and reliability.
Solution Approach 2:
The system performs preliminary validation and fault detection on task plans before full execution. By using natural language processing to analyze the generated task plans and predict potential issues, the system can correct problems before they affect actual task execution, improving reliability without reducing productivity.
3Ease of operation
If natural language processing is used for human interaction, then ease of operation is improved, but loss of information may increase due to ambiguous language interpretation
Solution Approach 1:
The natural language processing system is designed to request clarification and additional information when instructions are ambiguous. Rather than making assumptions, the system actively seeks to resolve ambiguities by asking users for clarification, ensuring accurate understanding while maintaining ease of operation through natural language communication.
Data Source
AI summary
Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters and/or instructions may advantageously be specified in natural language (NL) and communicated with the LLM via an NL prompt or query. The LLM module provides a task plan in NL, which can be evaluated for at least one fault or error. If at least one fault or error is identified, the LLM module can be queried to provide a resolution.