Robot Skill Knowledge Base for Semantic Relation Learning
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Solution Overview
Problem
Current robot learning from demonstration (LfD) methods are limited in scalability and require numerous demonstrations across different contexts, making it difficult for novice users to efficiently teach new skills to robots, especially in generalizing physical skills to novel situations.
Innovation Solution
A novel interactive learning paradigm that allows novice users to teach new semantic relations in a knowledge base by observing a demonstration, generating hypotheses for using skills in different contexts, and receiving confirmation or rejection, thereby updating the knowledge base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional LfD methods are used to teach new skills to robots, then the robot can learn physical skills through observation, but numerous demonstrations across different contexts are required, making the process time-consuming and difficult for novice users
Solution Approach 1:
The patent inverts the traditional LfD information flow by having the robot generate hypotheses about skill applications and seeking confirmation from the user, rather than requiring the user to provide numerous demonstrations. The robot actively queries the user about potential skill transfers to new contexts, reversing the teaching direction and significantly reducing the time and effort required from novice users.
Solution Approach 2:
The system implements an active feedback loop where the robot generates hypotheses about skill generalization and receives confirmation or correction from the user. This feedback mechanism allows the robot to iteratively refine its understanding of physical skills and their applicable contexts, achieving high generalization capability with minimal demonstration time.
2Adaptability or versatility
If numerous demonstrations with different movement trajectories and objects are performed to achieve generalization, then the robot can learn to apply skills to novel situations, but the scalability is limited and the complexity of the teaching process increases
Solution Approach 1:
The robot performs preliminary analysis of the demonstrated skill to extract semantic relationships between objects, actions, and effects before encountering new situations. By pre-processing the demonstration data to identify generalizable patterns and relationships, the robot reduces the need for numerous contextual demonstrations and simplifies the teaching process while maintaining high adaptability.
Solution Approach 2:
The patent introduces semantic relationships as an intermediary layer between the physical demonstration and the skill application. This semantic representation acts as a mediator that captures the essential meaning of the skill transfer, allowing the robot to generalize to novel situations without requiring direct exposure to each specific context, thereby reducing teaching complexity.
3Adaptability or versatility
If multiple teachers perform demonstrations to improve generalization, then the robot can learn from diverse perspectives, but the scalability of the approach is limited and requires coordination of multiple resources
Solution Approach 1:
The robot performs self-service by autonomously generating hypotheses about skill applications and identifying which contexts warrant user confirmation. This self-directed approach eliminates the need for multiple teachers to coordinate demonstrations, as the robot independently determines the most valuable learning opportunities, significantly improving teaching efficiency while maintaining adaptability.
Data Source
AI summary
The present invention provides a robot system for learning one or more new semantic relations in a knowledge base of physical skills. The system is configured to obtain knowledge on a physical skill by observing a demonstration, by a human user, of the physical skill, update the knowledge base with the obtained knowledge, generate a hypothesis of using the physical skill in a different context compared to a context of the demonstration by performing similarity considerations on the knowledge base using the obtained knowledge, present the hypothesis to the human user, receive a confirmation or a refusal from the human user with regard to the presentation of the hypothesis to the human user, and update the knowledge base according to the hypothesis in case of receiving the confirmation or discard the hypothesis in case of receiving the refusal.