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Published April 2021 | public
Journal Article Open

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


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.

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.

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August 20, 2023
August 20, 2023