Robot Skill Knowledge Base Learning Through Semantic Hypothesis Feedback
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Solution Overview
Problem
Current robot learning from demonstration (LfD) methods are limited in scalability and require numerous demonstrations with different movement trajectories and objects, making it difficult for novice users to efficiently teach new skills to robots, especially in generalizing skills to novel situations.
Innovation Solution
A novel interactive learning paradigm that allows novice users to teach new semantic relations in a knowledge base of physical skills by observing a demonstration, generating hypotheses for using skills in different contexts, and updating the knowledge base based on user confirmation or rejection, enabling faster skill acquisition and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If numerous demonstrations with different movement trajectories and objects are used for robot learning, then the robot can learn skills better, but the time and complexity required for teaching increases significantly
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. This inversion allows the robot to actively learn semantic relations with far fewer user interactions.
Solution Approach 2:
The system implements feedback loops where the robot generates hypotheses about skill contexts, receives confirmation or correction from the user, and updates its knowledge base accordingly. This feedback mechanism enables efficient learning without requiring extensive pre-programming or demonstrations.
2Reliability
If numerous demonstrations with different movement trajectories and objects are used for robot learning, then the robot can learn skills better, but the complexity of the teaching process increases
Solution Approach 1:
The patent reverses the traditional approach by having the robot generate hypotheses about skill applications and seek user confirmation, rather than requiring users to provide numerous demonstrations. This inversion simplifies the teaching process while maintaining learning accuracy.
Solution Approach 2:
The robot performs self-learning by generating its own hypotheses about semantic relations and skill contexts, reducing the burden on the user. The system autonomously updates its knowledge base based on user feedback, making the teaching process simpler and more scalable.
3Device complexity
If traditional LfD methods are used without active learning phase, then the implementation is simpler, but the scalability is limited to observed demonstrations
Solution Approach 1:
The system transitions from a static knowledge base to a dynamic learning system that continuously updates semantic relations based on user feedback. The robot can adapt to new contexts and generalize skills beyond observed demonstrations through hypothesis generation and confirmation loops.
Solution Approach 2:
By implementing feedback loops where the robot generates hypotheses and receives user confirmation, the system enables scalable skill generalization. This feedback mechanism allows the robot to learn new semantic relations and apply skills to novel situations without requiring re-demonstration of each specific case.
Data Source
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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.