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• W3107626576 abstract "In this paper, we solve the problem of finding a certified control policy that drives a robot from any given initial state and under any bounded disturbance to the desired reference trajectory, with guarantees on the convergence or bounds on the tracking error. Such a controller is crucial in safe motion planning. We leverage the advanced theory in Control Contraction Metric and design a learning framework based on neural networks to co-synthesize the contraction metric and the controller for control-affine systems. We further provide methods to validate the convergence and bounded error guarantees. We demonstrate the performance of our method using a suite of challenging robotic models, including models with learned dynamics as neural networks. We compare our approach with leading methods using sum-of-squares programming, reinforcement learning, and model predictive control. Results show that our methods indeed can handle a broader class of systems with less tracking error and faster execution speed. Code is available at https://github.com/sundw2014/C3M." @default.
• W3107626576 created "2020-12-07" @default.
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• W3107626576 date "2020-11-25" @default.
• W3107626576 modified "2023-09-27" @default.
• W3107626576 title "Learning Certified Control using Contraction Metric" @default.
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• W3107626576 doi "https://doi.org/10.48550/arxiv.2011.12569" @default.
• W3107626576 hasPublicationYear "2020" @default.
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