Papers › A Framework for Learning and Reusing Robotic Skills
A Framework for Learning and Reusing Robotic Skills
Brendan Hertel, Nhu Tran, Meriem Elkoudi, Reza Azadeh
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In this paper, we present our work in progress towards creating a library of motion primitives. This library facilitates easier and more intuitive learning and reusing of robotic skills. Users can teach robots complex skills through Learning from Demonstration, which is automatically segmented into primitives and stored in clusters of similar skills. We propose a novel multimodal segmentation method as well as a novel trajectory clustering method. Then, when needed for reuse, we transform primitives into new environments using trajectory editing. We present simulated results for our framework with demonstrations taken on real-world robots.
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