LLM Robot Control for Natural-Language Task Planning and Fault Recovery
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Solution Overview
Problem
Current robot control systems lack the ability to effectively automate tasks that require complex decision-making and adaptability, such as task planning, motion planning, and human interaction, due to limitations in processing natural language inputs and handling faults in task execution.
Innovation Solution
A method and system that utilize a large language model (LLM) to generate and execute task plans in natural language, allowing robots to capture sensor data, generate environmental descriptions, and interact with users through natural language queries, while identifying and resolving faults in task execution by generating and executing updated plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional robot control systems are used, then the system structure is simple, but the ability to perform complex decision-making and adaptability is insufficient
Solution Approach 1:
A natural language processing intermediary layer is introduced between the sensor data and the robot control system. This intermediary translates complex sensor data and control commands into natural language that can be processed by the LLM, enabling complex decision-making without directly complicating the control system architecture.
Solution Approach 2:
The patent replaces traditional mechanical control systems with an AI-based LLM system. Instead of using complex rule-based control algorithms and decision trees, the system uses a large language model to process natural language inputs and generate control commands, substituting mechanical control logic with neural network-based reasoning.
2Productivity
If LLM is integrated into robot control, then task planning and adaptability are improved, but processing time and computational resources increase
Solution Approach 1:
The LLM is pre-trained on extensive datasets related to robot operations, task planning, and domain-specific knowledge. This preliminary training allows the model to quickly generate accurate task plans and decisions during runtime without requiring extensive real-time computation, reducing processing time while maintaining high productivity.
3Ease of operation
If LLM is used for natural language processing, then human interaction capability is enhanced, but system reliability and fault handling become more challenging
Solution Approach 1:
The system implements a feedback loop where the LLM's task plans and decisions are monitored for faults and errors. When faults are detected, the system provides feedback to the LLM with correction information and alternative approaches, allowing the model to learn from mistakes and improve reliability while maintaining natural language interaction capabilities.
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.