Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution
This work addresses the need for intuitive and efficient robot learning from demonstrations in robotics, though it appears incremental as it builds on existing RLfD methods with a specific modeling approach.
The authors tackled the problem of enabling collaborative robots to learn trajectory-based skills from human demonstrations by developing a novel interactive framework that extracts key-points and uses Hidden Markov Models, resulting in a learned model capable of producing generalized trajectories.
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.