LLM-Guided Robot Control for Adaptive Task and Motion Planning
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Solution Overview
Problem
Current robot control systems lack the ability to efficiently automate tasks that require complex decision-making and adaptability, such as task planning, motion planning, and human interaction, due to limitations in their programming and instruction sets.
Innovation Solution
The integration of a large language model (LLM) into robot systems, which enables natural language processing and generation of task plans by capturing sensor data, generating natural language descriptions, and interacting with the LLM to break down tasks into executable instructions, allowing for adaptive and complex task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional robot control systems with fixed programming and instruction sets are used, then device complexity is reduced and ease of operation is improved, but adaptability and versatility are severely limited
Solution Approach 1:
The patent introduces a large language model (LLM) as an intermediary between human natural language input and the robot's executable instruction set. The LLM translates high-level natural language descriptions into sequences of robot-specific instructions, enabling the robot to perform complex, adaptable tasks without requiring complex programming in the robot itself. This resolves the contradiction by centralizing intelligence in the LLM layer while keeping the robot control system relatively simple.
2Adaptability or versatility
If traditional robot control systems with fixed instruction sets are used, then device complexity is minimized, but the ability to perform complex decision-making and adaptive tasks is insufficient
Solution Approach 1:
The patent segments the control architecture into distinct layers: the LLM layer for high-level task planning and decision-making, and the robot control layer for executing specific instructions. This segmentation allows complex adaptive behavior to be achieved through the LLM while the robot's control system remains relatively simple and focused on execution. The LLM handles task decomposition, motion planning, and human interaction, while the robot executes standardized instructions.
3Ease of operation
If natural language processing capabilities are added to enable human interaction and task planning, then adaptability and ease of operation are improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent employs a universal LLM that can handle multiple functions including natural language understanding, task planning, motion planning, and human interaction through a single integrated system. This multi-functional approach improves ease of operation by allowing humans to interact with the robot using natural language for various tasks, while avoiding the need to implement separate complex systems for each function. The LLM serves as a universal interface that adapts to different task requirements.
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 a recursive sequence of NL prompts or queries. Corresponding NL responses from the LLM may then be converted into robot control parameters and/or instructions. In this way, an LLM may be leveraged by the robot control system to enhance the autonomy of various operations and/or functions, including without limitation task planning, motion planning, human interaction, and/or reasoning about the environment.