Multimodal Skill Conversion for Cross-Domain Robot Adaptation
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Solution Overview
Problem
Cross-domain zero-shot adaptation in autonomous driving and robotics is challenging due to task complexity and dynamic environments, with existing techniques requiring expert data and limited to short, simple tasks, and facing performance degradation in new domains.
Innovation Solution
An apparatus and method for skill conversion using an encoder to embed prompts in various modalities, generating semantic skill sequences based on skill-level language instructions, and calculating probabilities for executing actions in target domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If cross-domain zero-shot adaptation is implemented, then immediate adaptation without additional data collection is achieved, but performance degradation occurs in new domains due to task complexity and dynamic environments
Solution Approach 1:
The system performs preliminary encoding of source domain demonstrations into skill-level language instructions and semantic skill sequences before encountering the target domain. This pre-processing creates reusable knowledge representations that can be immediately applied to new domains without additional data collection, while maintaining reliability through the structured skill decomposition framework
Solution Approach 2:
The patent introduces skill-level language instructions and semantic skill sequences as intermediary representations between source and target domains. These intermediaries bridge the domain gap by translating concrete demonstrations into abstract skill descriptions that can be adapted across different environments, preventing performance degradation through systematic knowledge transfer
2Measurement precision
If expert data from target domain is directly required, then accurate skill execution is achieved, but applicability to real-world situations is limited due to data availability constraints
Solution Approach 1:
The system extracts essential skill information from source domain demonstrations by converting them into skill-level language instructions and semantic skill sequences. This extraction process separates the core skill knowledge from domain-specific details, enabling accurate skill execution in target domains without requiring target domain expert data, thus improving real-world applicability
Solution Approach 2:
The patent creates copied representations of source domain skills in the form of semantic skill sequences that can be executed in target domains. These copied skill representations maintain the essential execution patterns while being adaptable to different environments, achieving both accuracy and versatility without requiring original domain data
3Adaptability or versatility
If Decision Transformer techniques are used for cross-domain adaptation, then knowledge transfer is achieved, but only short and simple tasks can be handled
Solution Approach 1:
The patent segments complex tasks into hierarchical skill-level components represented as language instructions and semantic skill sequences. This segmentation allows the system to handle complex tasks by breaking them down into manageable skill units that can be individually transferred and executed across domains, overcoming the limitation of handling only simple tasks while maintaining knowledge transfer capability
Data Source
AI summary
A method for skill conversion comprises receiving a multi-modal form of prompt including at least one of video data, text data, or sensor data from a user, converting the prompt into a skill-level language instruction using an encoder corresponding to each of the multi-modal and generating a semantic skill sequence to be executed in a target domain based on the skill-level language instruction.