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Improving Local Trajectory Optimisation using Probabilistic Movement Primitives

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Author
Shyam, RB Ashith
Lightbody, Peter
Das, Gautham
Liu, Pengcheng
Gomez-Gonzalez, Sebastian
Neumann, Gerhard
Date
2019-11-06
Acceptance date
2019-06-20
Type
Conference paper
acceptedVersion
Publisher
IEEE/RSJ
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Abstract
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.
Journal/conference proceeding
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) November 4-8, 2019, Macau, China;
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.
URI
https://www.iros2019.org/
http://hdl.handle.net/10369/10831
DOI
https://doi.org/10.1109/IROS40897.2019.8967980
Description
Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems, November 4 - 8, 2019, Macau, China, available at: https://doi.org/10.1109/IROS40897.2019.8967980.
Rights
© 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
Sponsorship
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
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  • School of Technologies Research [166]

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