Corpus ID: 52895099

Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution

  title={Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution},
  author={Sulabh Kumra and Ferat Sahin},
In robotics, there is need of an interactive and expedite learning method as experience is expensive. Robot Learning from Demonstration (RLfD) enables a robot to learn a policy from demonstrations performed by teacher. RLfD enables a human user to add new capabilities to a robot in an intuitive manner, without explicitly reprogramming it. In this work, we present a novel interactive framework, where a collaborative robot learns skills for trajectory based tasks from demonstrations performed by… Expand
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  • Markus Schneider, W. Ertel
  • Engineering, Computer Science
  • 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems
  • 2010
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We present a programming-by-demonstration framework for generically extracting the relevant features of a given task and for addressing the problem of generalizing the acquired knowledge to differentExpand
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