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arXiv · 1809.10797

Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution

Sulabh Kumra·Ferat Sahin
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Abstract

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 a human teacher. The robot extracts features from each demonstration called as key-points and learns a model of the demonstrated skill using Hidden Markov Model (HMM). Our experimental results show that the learned model can be used to produce a generalized trajectory based skill.

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Sulabh Kumra, Ferat Sahin. 2018-09-27. Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution. https://arxiv.org/abs/1809.10797

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