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.
@article{arxiv.1809.10797,
title = {Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution},
author = {Sulabh Kumra and Ferat Sahin},
journal= {arXiv preprint arXiv:1809.10797},
year = {2018}
}