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Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

Nakka, Yashwanth Kumar and Liu, Anqi and Shi, Guanya and Anandkumar, Anima and Yue, Yisong and Chung, Soon-Jo (2021) Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems. IEEE Robotics and Automation Letters, 6 (2). pp. 389-396. ISSN 2377-3766. https://resolver.caltech.edu/CaltechAUTHORS:20200526-150616242

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Abstract

Learning-based control algorithms require data collection with abundant supervision for training. Safe exploration algorithms ensure the safety of this data collection process even when only partial knowledge is available. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained stochastic optimal control with dynamics learning and feedback control. We derive an iterative convex optimization algorithm that solves an Information-cost Stochastic Nonlinear Optimal Control problem (Info-SNOC). The optimization objective encodes control cost for performance and exploration cost for learning, and the safety is incorporated as distributionally robust chance constraints. The dynamics are predicted from a robust regression model that is learned from data. The Info-SNOC algorithm is used to compute a sub-optimal pool of safe motion plans that aid in exploration for learning unknown residual dynamics under safety constraints. A stable feedback controller is used to execute the motion plan and collect data for model learning. We prove the safety of rollout from our exploration method and reduction in uncertainty over epochs, thereby guaranteeing the consistency of our learning method. We validate the effectiveness of Info-SNOC by designing and implementing a pool of safe trajectories for a planar robot. We demonstrate that our approach has higher success rate in ensuring safety when compared to a deterministic trajectory optimization approach.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/LRA.2020.3044033DOIArticle
https://arxiv.org/abs/2005.04374arXivDiscussion Paper
ORCID:
AuthorORCID
Nakka, Yashwanth Kumar0000-0001-7897-3644
Shi, Guanya0000-0002-9075-3705
Yue, Yisong0000-0001-9127-1989
Chung, Soon-Jo0000-0002-6657-3907
Additional Information:© 2020 IEEE. Manuscript receivedMay 8, 2020; accepted October 1, 2020. Date of publication December 10, 2020; date of current version December 28, 2020. This letter was recommended for publication by Associate Editor L. Tapia and Editor N. Amato upon evaluation of the reviewers’ comments. This work was supported by the Jet Propulsion Laboratory, Caltech and the Raytheon Company. The work of Anqi Liu was supported by a PIMCO Postdoctoral Fellowship. We acknowledge the contribution of Irene S. Crowell in implementing Info-SNOC.
Group:GALCIT, Center for Autonomous Systems and Technologies (CAST)
Funders:
Funding AgencyGrant Number
JPL/CaltechUNSPECIFIED
Raytheon CompanyUNSPECIFIED
PIMCOUNSPECIFIED
Issue or Number:2
Record Number:CaltechAUTHORS:20200526-150616242
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200526-150616242
Official Citation:Y. K. Nakka, A. Liu, G. Shi, A. Anandkumar, Y. Yue and S. -J. Chung, "Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems," in IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 389-396, April 2021, doi: 10.1109/LRA.2020.3044033
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103472
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:26 May 2020 22:31
Last Modified:05 Jan 2021 23:25

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