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Learning to jump in granular media: Unifying optimal control synthesis with Gaussian process-based regression

Chang, Alexander H. and Hubicki, Christian M. and Aguilar, Jeff J. and Goldman, Daniel I. and Ames, Aaron D. and Vela, Patricio A. (2017) Learning to jump in granular media: Unifying optimal control synthesis with Gaussian process-based regression. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE , Piscataway, NJ, pp. 2154-2160. ISBN 978-1-5090-4633-1. https://resolver.caltech.edu/CaltechAUTHORS:20190205-083656859

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Abstract

The varied and complex dynamics of deformable terrain are significant impediments toward real-world viability of locomotive robotics, particularly for legged machines. We explore vertical jumping on granular media (GM) as a model task for legged locomotion on uncharacterized deformable terrain. By integrating (Gaussian process) GP-based regression and evaluation to estimate ground forcing as a function of state, a one-dimensional jumper acquires the ability to learn forcing profiles exerted by its environment in tandem to achieving its control objective. The GP-based dynamical model initially assumes a baseline rigid, non-compliant surface. As part of an iterative procedure, the optimizer employing this model generates an optimal control to achieve a target jump height while respecting known hardware limitations of the robot model. Trajectory and forcing data recovered from evaluation on the true GM surface model simulation is applied to train the GP, and in turn, provide the optimizer a more richly informed dynamical model of the environment. After three iterations, predicted optimal control trajectories coincide with execution results, within 1.2% jumping height error, as the GP-based approximation converges to the true GM model.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/icra.2017.7989248DOIArticle
ORCID:
AuthorORCID
Ames, Aaron D.0000-0003-0848-3177
Additional Information:© 2017 IEEE. This work was supported by NSF grant CPS#1544857.
Funders:
Funding AgencyGrant Number
NSFCPS-1544857
Record Number:CaltechAUTHORS:20190205-083656859
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190205-083656859
Official Citation:A. H. Chang, C. M. Hubicki, J. J. Aguilar, D. I. Goldman, A. D. Ames and P. A. Vela, "Learning to jump in granular media: Unifying optimal control synthesis with Gaussian process-based regression," 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, pp. 2154-2160. doi: 10.1109/ICRA.2017.7989248
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92654
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:05 Feb 2019 19:12
Last Modified:03 Oct 2019 20:46

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