Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks

Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks

We present a framework for automatically learning human user models from joint-action demonstrations that enables a robot to compute a robust policy for a collaborative task with a human. First, the demonstrated action sequences are clustered into different human types using an unsupervised learning...

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Bibliographic Details
Main Authors: Shah, Julie A (Contributor), Nikolaidis, Stefanos (Contributor), Ramakrishnan, Ramya (Contributor), Gu, Keren (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2017-04-05T20:03:20Z.
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