LLM-Guided Robot Control for Natural Language Task Planning
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Solution Overview
Problem
Current robot control systems lack the ability to efficiently automate tasks that require complex decision-making, human interaction, and dynamic task planning, relying heavily on pre-programmed instructions and external control.
Innovation Solution
Integration of a large language model (LLM) that processes natural language inputs to generate task plans, allowing robots to understand environments, interpret instructions, and execute tasks autonomously by converting natural language descriptions into robot-specific control instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-programmed instructions and external control are used, then robot control is simple and reliable, but the robot cannot efficiently automate tasks requiring complex decision-making and dynamic task planning
Solution Approach 1:
The patent introduces a large language model as an intermediary component between the robot's sensors/actuators and the control system. This LLM mediator processes natural language inputs, generates task plans, and translates high-level instructions into executable robot commands, enabling complex decision-making without requiring the entire control system to be fundamentally complex.
Solution Approach 2:
The control system is segmented into distinct functional modules: perception modules that gather environmental data, a large language model module that processes natural language and generates task plans, and execution modules that carry out specific actions. This segmentation allows each component to specialize in specific functions, improving overall adaptability while managing complexity through modular design.
2Adaptability or versatility
If pre-programmed instructions are used, then the control system is easy to operate, but the robot cannot understand natural language inputs or adapt to new situations
Solution Approach 1:
The patent replaces traditional mechanical programming interfaces with a cognitive system based on large language models. Instead of requiring users to program robots using formal languages or complex interfaces, the system substitutes this with natural language processing capabilities, allowing users to interact with the robot using everyday language while the LLM handles the complexity of translation and interpretation.
3Extent of automation
If external control is used, then the robot system is simple to control, but the robot cannot execute tasks autonomously
Solution Approach 1:
The system performs preliminary processing of task information through the large language model, which pre-generates task plans and decomposes high-level goals into executable steps before actual robot execution begins. This preliminary cognitive processing enables autonomous operation while keeping the real-time control system relatively simple, as the complex decision-making work is done in advance.
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. An NL response 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.