Robot Control With LLM Task Planning for Dynamic Autonomy
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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) into robot systems to generate natural language descriptions of environments and task objectives, allowing for the generation of task plans that can be executed by the robot, enabling autonomous decision-making and human-like interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If pre-programmed instructions and external control are used, then robot control reliability is maintained, but task automation capability and adaptability deteriorate
Solution Approach 1:
A language model serves as an intermediary between natural language instructions and robot control systems. The language model translates human language into structured task plans and control commands, enabling high-level automation without requiring complex programming interfaces. This mediator handles the complexity of task understanding and decomposition, allowing robots to execute automated tasks with natural language inputs.
Solution Approach 2:
The robot system performs self-planning and self-control by using the language model to generate task plans autonomously from natural language descriptions. The system can independently decompose high-level goals into executable steps, select appropriate actions, and adjust behavior based on environmental feedback, reducing the need for external control programming while maintaining reliable operation.
2Ease of operation
If natural language processing is integrated, then human interaction capability is improved, but processing time and computational resources increase
Solution Approach 1:
The language model is pre-trained on extensive language corpora before deployment, enabling it to process natural language efficiently during robot operation. This preliminary training establishes the foundational language understanding and generation capabilities, so that during actual robot-human interaction, the system can quickly translate and respond without requiring extensive real-time computation for basic language processing.
Solution Approach 2:
The natural language processing task is segmented into distinct stages: language input reception, task plan generation, action decomposition, and control command formulation. Each segment can be processed independently and in parallel where possible, reducing overall processing time. The segmentation also allows for optimization at each stage without affecting the entire system.
3Adaptability or versatility
If LLM-based task planning is implemented, then adaptability to dynamic environments is improved, but control precision and reliability may deteriorate
Solution Approach 1:
The robot system implements feedback loops where the language model continuously receives information about task execution status and environmental changes. Based on this feedback, the system can adjust task plans, refine action sequences, and correct deviations from intended behavior. This feedback mechanism maintains reliability by ensuring that adaptive decisions are grounded in actual system state and performance data.
Solution Approach 2:
The control system transitions from static pre-programmed instructions to dynamic language-model-generated task plans that can adapt in real-time. The language model generates flexible action sequences that can be modified based on environmental feedback, while maintaining structured output formats that ensure reliable execution. This dynamic approach allows the system to balance adaptability with control precision.
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.