Robot Skill Grounding for Feasible Natural Language Control
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Solution Overview
Problem
Current language models lack real-world experience and grounding, making them ineffective in controlling robots to perform tasks with high-level, free-form natural language instructions, as they may suggest actions that are not feasible or appropriate for the robot's capabilities and environment.
Innovation Solution
The implementation leverages large language models to generate task-grounding and world-grounding measures for robotic skills, ensuring that selected skills are both feasible and contextually appropriate by considering the robot's capabilities and environmental conditions, using pre-trained robotic skills and value functions to refine the language model output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If language models are used to process natural language instructions and generate task steps, then the robot can understand high-level instructions, but the language model may generate actions that are not feasible or appropriate for the robot's capabilities and environment
Solution Approach 1:
The patent introduces an intermediary system between the language model and robot execution that includes: (1) a robot capability database storing pre-defined skills and their requirements, (2) an environment sensor system that captures current state information, and (3) a validation module that cross-references LLM-generated steps against robot capabilities and environmental conditions. This intermediary layer filters and validates the LLM output before execution, ensuring only feasible and appropriate actions are performed.
Solution Approach 2:
The patent implements feedback mechanisms where: (1) environmental sensors continuously monitor the current state and provide feedback to the validation module, (2) the robot's actual capabilities and current task progress are fed back to verify whether LLM-generated steps are still appropriate, and (3) execution results are fed back to refine future validation. This closed-loop feedback ensures the robot can adapt to changing conditions and reject infeasible actions.
2Loss of information
If the language model generates detailed task steps, then the task completion guidance is improved, but the complexity of validating and selecting appropriate robotic skills increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing robot capabilities into a structured database before the LLM generates task steps. The robot skills, their parameters, prerequisites, and environmental requirements are all pre-defined and stored in an organized manner. This preliminary structuring allows the validation module to efficiently cross-reference LLM-generated steps against the pre-organized capability database, reducing the complexity of real-time validation while maintaining comprehensive task guidance.
3Reliability
If the robot uses pre-trained robotic skills, then the reliability of task execution is improved, but the adaptability to new or varied tasks decreases
Solution Approach 1:
The patent implements universality by designing a modular robot skill database where skills are broken down into reusable components with standardized interfaces. Each pre-trained skill is defined with clear parameters, prerequisites, and output specifications that can be combined in different sequences to accomplish varied tasks. The LLM generates task plans that orchestrate these modular skills in different configurations depending on the specific task requirements, allowing the same set of pre-trained skills to adapt to diverse task scenarios while maintaining execution reliability.
Data Source
AI summary
Implementations process, using a large language model, a free-form natural language (NL) instruction to generate to generate LLM output. Those implementations generate, based on the LLM output and a NL skill description of a robotic skill, a task-grounding measure that reflects a probability of the skill description in the probability distribution of the LLM output. Those implementations further generate, based on the robotic skill and current environmental state data, a world-grounding measure that reflects a probability of the robotic skill being successful based on the current environmental state data. Those implementations further determine, based on both the task-grounding measure and the world-grounding measure, whether to implement the robotic skill.