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In robotics, there is need of an interactive and expedite learning method asexperience is expensive. Robot Learning from Demonstration (RLfD) enables arobot to learn a policy from demonstrations performed by teacher. RLfD enablesa human user to add new capabilities to a robot in an intuitive manner, withoutexplicitly reprogramming it. In this work, we present a novel interactiveframework, where a collaborative robot learns skills for trajectory based tasksfrom demonstrations performed by a human teacher. The robot extracts featuresfrom each demonstration called as key-points and learns a model of thedemonstrated skill using Hidden Markov Model (HMM). Our experimental resultsshow that the learned model can be used to produce a generalized trajectorybased skill.

Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution