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Improving Local Trajectory Optimisation using Probabilistic Movement Primitives

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conference contribution
posted on 04.04.2022, 10:51 authored by RB Ashith Shyam, Peter Lightbody, Gautham Das, Pengcheng Liu, Sebastian Gomez-Gonzalez, Gerhard Neumann
Local trajectory optimisation techniques are a powerful tool for motion planning. However, they often get stuck in local optima depending on the quality of the initial solution and consequently, often do not find a valid (i.e.collision free) trajectory. Moreover, they often require fine tuning of a cost function to obtain the desired motions. In this paper, we address both problems by combining local trajectory optimisation with learning from demonstrations. The human expert demonstrates how to reach different target end-effector locations in different ways. From these demonstrations, we estimate a trajectory distribution, represented by a Probabilistic Movement Primitive (ProMP). For a new target location, we sample different trajectories from the ProMP and use these trajectories as initial solutions for the local optimisation. As the ProMP generates versatile initial solutions for the optimisation,the chance of finding poor local minima is significantly reduced.Moreover, the learned trajectory distribution is used to specify the smoothness costs for the optimisation, resulting in solutions of similar shape than the demonstrations. We demonstrate the effectiveness of our approach in several complex obstacle avoidance scenarios.

History

Presented at

Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems, November 4 - 8, 2019, Macau, China

Link to Conference Website

Published in

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) November 4-8, 2019, Macau, China

Publisher

IEEE/RSJ

Version

AM (Accepted Manuscript)

Citation

Shyam, R.B.A., Lightbody, P., Das, G., Liu, P., Gomez-Gonzalez, S. and Neumann, G. (2019) 'Improving Local Trajectory Optimisationusing Probabilistic Movement Primitives', IEEE/RSJ International Conference on Intelligent Robots and Systems, November 4 - 8, Macau, China.

Electronic ISSN

2153-0866

ISBN

978-1-7281-4004-9

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Pengcheng Liu

Copyright Holder

© The Publisher

Publisher Rights Statement

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Language

en