Adaptation and Robust Learning of Probabilistic Movement Primitives

TL;DR

This paper introduces a robust probabilistic movement primitive framework that efficiently adapts to environmental changes and generalizes from few demonstrations, demonstrated through coffee making and table tennis tasks.

Contribution

It presents a novel method using prior distributions for robust parameter estimation and introduces general-purpose operators for movement adaptation in robotics.

Findings

Achieved successful adaptation with only two demonstrations in coffee tasks.

Outperformed previous methods in robot table tennis hit and return rates.

Demonstrated effective generalization to environmental changes.

Abstract

Probabilistic representations of movement primitives open important new possibilities for machine learning in robotics. These representations are able to capture the variability of the demonstrations from a teacher as a probability distribution over trajectories, providing a sensible region of exploration and the ability to adapt to changes in the robot environment. However, to be able to capture variability and correlations between different joints, a probabilistic movement primitive requires the estimation of a larger number of parameters compared to their deterministic counterparts, that focus on modeling only the mean behavior. In this paper, we make use of prior distributions over the parameters of a probabilistic movement primitive to make robust estimates of the parameters with few training instances. In addition, we introduce general purpose operators to adapt movement primitives in joint and task space. The proposed training method and adaptation operators are tested in a coffee preparation and in robot table tennis task. In the coffee preparation task we evaluate the generalization performance to changes in the location of the coffee grinder and brewing chamber in a target area, achieving the desired behavior after only two demonstrations. In the table tennis task we evaluate the hit and return rates, outperforming previous approaches while using fewer task specific heuristics.