LLM Robot Control for Natural Language Task Planning
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Solution Overview
Problem
Existing robot systems lack efficient methods to automate tasks, motion planning, human interaction, and logic reasoning, limiting their ability to perform a wide range of tasks autonomously.
Innovation Solution
Integrating a large language model (LLM) to process natural language inputs and generate task plans, breaking down complex tasks into reusable work primitives, and converting them into executable instructions for robots.
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 robot cannot autonomously perform complex tasks across diverse domains
Solution Approach 1:
The patent introduces a large language model as an intermediary component between the user's natural language input and the robot's execution system. This LLM mediator translates high-level task descriptions into structured task plans with subtasks and subsubtasks, enabling the robot to perform complex autonomous operations without requiring complex traditional control system architecture.
2Manufacturing precision
If complex tasks are broken down into detailed subtasks, then task execution precision improves, but the planning time and computational load increase
Solution Approach 1:
The patent applies segmentation by automatically decomposing complex tasks into hierarchical subtasks and subsubtasks using the large language model. This segmentation enables precise task execution by breaking down complex operations into manageable steps while the LLM efficiently handles the decomposition process, balancing detail with planning speed.
Solution Approach 2:
The system performs preliminary action by pre-defining a library of work primitives and task templates that can be reused across different tasks. When a new task is received, the LLM leverages these pre-existing structures to quickly generate task plans without starting from scratch, thereby reducing planning time while maintaining execution precision.
3Ease of operation
If natural language processing is used for human-robot interaction, then ease of operation improves, but reliability of task execution may decrease due to ambiguity
Solution Approach 1:
The patent implements feedback mechanisms where the large language model processes natural language inputs and generates structured task plans that can be reviewed and refined. The system provides feedback loops that allow for clarification and adjustment of task interpretations, maintaining ease of natural language interaction while improving execution reliability through iterative refinement and confirmation.
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.