Learn-To-Propagate: Distributionally Robust Motion Planning for Nonlinear Uncertain Systems
Overview
This research presents Learn-To-Propagate Distributionally-Robust Forest (L2P-DR-Forest), a novel sampling-based planning algorithm designed for nonlinear stochastic systems with unknown initial states and process noise distributions. The approach utilizes optimal transport to learn ambiguity sets containing true system distributions with high confidence, propagates these sets through dynamics, and verifies safety. It integrates these techniques into a forest of search trees in the state space while mitigating ambiguity-set growth. The authors prove the algorithm's correctness and probabilistic completeness under certain assumptions. Evaluations on robotic benchmarks demonstrate that L2P-DR-Forest significantly outperforms existing methods while providing formal guarantees with high confidence.
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- arXiv Robotics — research abstractsSignalLearn-To-Propagate: Distributionally Robust Motion Planning for Nonlinear Uncertain Systems
This research presents Learn-To-Propagate Distributionally-Robust Forest (L2P-DR-Forest), a novel sampling-based planning algorithm designed for nonlinear stochastic systems with unknown initial states and process noise distributions. The approach utilizes optimal transport to learn ambiguity sets containing true system distributions with high confidence, propagates these sets through dynamics, and verifies safety. It integrates these techniques into a forest of search trees in the state space while mitigating ambiguity-set growth. The authors prove the algorithm's correctness and probabilistic completeness under certain assumptions. Evaluations on robotic benchmarks demonstrate that L2P-DR-Forest significantly outperforms existing methods while providing formal guarantees with high confidence.
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