Probabilistic progress prediction and sequencing of concurrent movement primitives
Simon Manschitz, Jens Kober, Michael Gienger, Jan Peters
Abstract
Classical approaches towards learning coordinated movement tasks often represent a movement in a sequential and exclusive fashion. Introducing concurrency allows to decompose such tasks into a number of separate sequences, for instance for two different end-effectors. While this results in a compact and generic representation of the individual movement primitives (MPs), it is a hard problem to learn their temporal and causal organization. This paper presents a concept for learning movement tasks that require the coordination of several controlled effectors of a robot. We firstly introduce a concept to learn and estimate the progress of individual MPs from a low number of demonstrations. Secondly, we propose a representation of the task that incorporates several concurrent sequences of MPs. Combining these two elements allows to learn and reproduce coordinated bi-manual movement tasks robustly. The synchronization of the concurrent MPs is achieved implicitly using the progress prediction. The approach is evaluated in two simulation studies with a 25 degrees of freedom two-arm robot performing a pick-and-place task.
BibTeX
@inproceedings{iros2015_probabilisticpro,
title = {Probabilistic progress prediction and sequencing of concurrent movement primitives},
author = {Simon Manschitz and Jens Kober and Michael Gienger and Jan Peters},
booktitle = {IROS 2015},
year = {2015}
}