Robot Skill Learning via Natural Language Retrieval and Self-Improvement
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Solution Overview
Problem
Existing systems lack an effective method to bridge the gap between high-level natural language instructions and low-level robotic control parameters, requiring manual tuning and expertise for nuanced robot behavior, limiting intuitive user control.
Innovation Solution
A computer-implemented method using generative models to translate natural language requests into robotic control parameters by processing robot execution traces, generating summaries, and modulating base policies with modulation values to achieve desired behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning of modulation values is used to achieve nuanced robot behavior, then control precision is improved, but ease of operation deteriorates due to requiring technical expertise and iterative trial and error
Solution Approach 1:
The patent introduces natural language as an intermediary between the user and the complex modulation values. Instead of directly manipulating numerical parameters, users provide high-level semantic descriptions of desired robot behaviors, and the system automatically translates these into appropriate modulation values through generative models and retrieval mechanisms.
Solution Approach 2:
The system performs self-service by automatically generating and optimizing modulation values based on natural language requests without requiring manual intervention. The generative model retrieves relevant information from stored summaries and synthesizes appropriate control parameters autonomously, eliminating the need for expert users to manually tune each parameter.
2Ease of operation
If generative models and retrieval mechanisms are implemented to translate natural language to control parameters, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary action by pre-processing robot execution traces and generating natural language summaries that are stored for future retrieval. This advance preparation creates a knowledge base that the generative model can query during operation, enabling natural language control without requiring complex real-time processing of raw sensor data.
Solution Approach 2:
The patent uses copying by creating natural language summaries as textual representations of robot execution traces. These summaries serve as simplified copies that capture essential information about past robot behaviors and corresponding modulation values, allowing the system to reason about and retrieve appropriate control parameters without dealing with complex raw data structures.
3Adaptability or versatility
If natural language summaries of robot execution traces are stored and retrieved, then adaptability is improved for handling diverse user requests, but loss of time occurs during the retrieval and processing of summaries
Solution Approach 1:
The system performs preliminary action by pre-generating natural language summaries of robot execution traces and storing them in a database during idle periods or offline. When a user provides a natural language request, the system can quickly retrieve relevant pre-processed summaries instead of analyzing raw execution traces in real-time, significantly reducing response time while maintaining adaptability to diverse requests.
Data Source
AI summary
Implementations are provided for an interactive machine learning methodology that allows non-expert users to use natural language to teach new skills, particularly to robots, through language grounding and understanding. In various implementations, a plurality of natural language summaries may be retrieved. Each of the natural language summaries may describe details of robotic performance of a task, and may include, or be usable to retrieve, a corresponding set of reference modulation values. A set of modulation values corresponding to a natural language request may be generated based on the plurality of natural language summaries. The natural language request may specify one or more constraints on robotic performance of the task. A robot control signal may be generated based on the generated set of modulation values.