Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution

TL;DR

This paper introduces an interactive robot learning framework where a robot learns trajectory skills from human demonstrations using Hidden Markov Models, enabling intuitive skill acquisition without explicit programming.

Contribution

It presents a novel approach combining demonstration-based learning with HMMs for trajectory skills, enhancing robot adaptability and ease of learning.

Findings

The learned HMM-based model generalizes demonstrated trajectories.

The framework allows intuitive skill acquisition from human demonstrations.

Experimental results validate effective trajectory reproduction.

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.