Robot Task Planning With LLM-Based Parameter Reuse
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Solution Overview
Problem
Current robot control systems lack the ability to efficiently and autonomously perform diverse tasks across various environments due to limitations in adapting task plans to changing conditions and objects, relying heavily on pre-defined instructions and lacking adaptability.
Innovation Solution
The implementation of a robot system that utilizes a large language model (LLM) to generate natural language descriptions of the environment and task objectives, allowing for the parameterization and execution of task plans through natural language queries, enabling the robot to adapt and complete tasks by populating task plans with environmental nouns and verbs, and executing them autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-defined instructions are used for robot control, then the control system is simple to implement, but the adaptability to changing conditions and objects is poor
Solution Approach 1:
A language model serves as an intermediary between natural language instructions and robot control commands. The language model processes user input and generates appropriate task plans without requiring complex pre-programmed instruction sets, thereby improving adaptability while maintaining control system simplicity
Solution Approach 2:
The robot system autonomously generates task plans by querying the language model with natural language descriptions of the environment and objectives. This self-service mechanism eliminates the need for complex external control systems while enabling the robot to adapt to various conditions and objects
2Productivity
If task plans are parameterized and stored for reuse, then productivity increases through efficient task execution, but the complexity of managing and indexing task plans increases
Solution Approach 1:
Successful task plans are parameterized and stored as reusable templates. When similar tasks are encountered, the system retrieves and populates these templates with specific parameters, avoiding the need to create new task plans from scratch and thereby improving productivity without requiring complex management systems
Solution Approach 2:
Task plans are stored in parameterized form where specific objects and conditions can be replaced by variables. The system manages complexity by only storing the structural framework of tasks while allowing flexible parameter substitution, enabling efficient reuse without extensive task plan management
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, environment details, and/or instruction sets 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 a task plan. A task plan that successfully completes a first instance of a work objective may be parameterized and re-used to complete a second instance of the work objective. Parameterization of a task plan may include replacing one or more nouns/objects in the NL task plan with variables, while optionally preserving one or more verbs/actions in the NL task plan.