TEAM: a parameter-free algorithm to teach collaborative robots motions from user demonstrations

TL;DR

This paper introduces a parameter-free learning from demonstrations method for collaborative robots, enabling non-experts to teach robot motions effectively without manual tuning, demonstrated through industrial field tests.

Contribution

A novel parameter-free LfD approach based on probabilistic movement primitives using Jensen-Shannon divergence and Bayesian optimization, eliminating the need for manual parameter tuning.

Findings

High accuracy in reproducing motions with errors up to 0.28 degrees

Successful application in industrial tasks like elevator door maintenance

Positive user feedback on ease of use and motion accuracy

Abstract

Learning from demonstrations (LfD) enables humans to easily teach collaborative robots (cobots) new motions that can be generalized to new task configurations without retraining. However, state-of-the-art LfD methods require manually tuning intrinsic parameters and have rarely been used in industrial contexts without experts. We propose a parameter-free LfD method based on probabilistic movement primitives, where parameters are determined using Jensen-Shannon divergence and Bayesian optimization, and users do not have to perform manual parameter tuning. The cobot's precision in reproducing learned motions, and its ease of teaching and use by non-expert users are evaluated in two field tests. In the first field test, the cobot works on elevator door maintenance. In the second test, three factory workers teach the cobot tasks useful for their daily workflow. Errors between the cobot and target joint angles are insignificant -- at worst 0.28 deg -- and the motion is accurately reproduced -- GMCC score of 1. Questionnaires completed by the workers highlighted the method's ease of use and the accuracy of the reproduced motion. Public implementation of our method and datasets are made available online.