LLM-Based Robot Task Plans for Reusable Object Adaptation
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Solution Overview
Problem
Current robot control systems lack the ability to efficiently adapt and execute task plans in varying environments, particularly in scenarios where the objects or tasks differ from initial training conditions.
Innovation Solution
The integration of a large language model (LLM) into the robot control system, which generates natural language descriptions of the environment and task objectives, allowing the robot to parameterize and execute task plans dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robot control system uses fixed task plans trained on specific objects, then execution reliability for those objects is improved, but adaptability to new objects or environments deteriorates
Solution Approach 1:
The system changes parameters by replacing specific object identifiers in task plans with variable placeholders (e.g., changing 'pick up the cup' to 'pick up object'). This parameterization allows the same task plan structure to be reused across different objects by simply substituting the object parameter, thereby improving adaptability while maintaining execution reliability through proven task structures
Solution Approach 2:
The system creates universal task plans that can serve multiple functions and objects. By parameterizing task plans with variables instead of hardcoding specific objects, a single task plan can be applied to any object in the environment, making the robot system versatile and adaptable to new situations without requiring retraining for each specific object
2Adaptability or versatility
If the robot system generates task plans from scratch for each new task, then adaptability to new situations is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-defining task plan templates with variable placeholders for common task structures. When a new task arises, the system simply needs to fill in the specific object parameters rather than generating the entire task plan from scratch, significantly reducing processing time while maintaining adaptability to new situations
Solution Approach 2:
The system uses copying by replicating proven task plan structures and adapting them to new objects through parameter substitution. Instead of creating entirely new task plans, the system copies existing validated task plan templates and modifies the object parameters, thereby reducing computational overhead and generation time while maintaining adaptability
3Measurement precision
If the robot uses detailed natural language descriptions of environments, then task understanding accuracy is improved, but processing complexity and memory requirements increase
Solution Approach 1:
The system extracts only the essential elements needed for task execution from detailed environmental descriptions. By identifying and extracting key object parameters and task-relevant features while discarding extraneous information, the system maintains accurate task understanding without the full computational burden of processing complete detailed descriptions
Solution Approach 2:
The system segments environmental descriptions into discrete, manageable components such as object locations, properties, and task-relevant features. This segmentation allows the robot to process and utilize only the necessary information for task execution, reducing processing complexity while maintaining understanding accuracy through structured information organization
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.