Online Learning for Obstacle Avoidance

David Snyder, Wenhan Xia, Daniel Suo, Anirudha Majumdar, Elad Hazan

Keywords: obstacle avoidance, online optimization, regret minimization

TL;DR: Regret bounds for online learning obstacle avoidance policies

Abstract: We approach the fundamental problem of obstacle avoidance for robotic systems via the lens of online learning. In contrast to prior work that either assumes worst-case realization of uncertainty in the environment or a given stochastic model of uncertainty, we propose a method that is efficient to implement and provably grants instance-optimality to perturbations of trajectories generated from an open-loop planner in the sense of minimizing worst-case regret. The resulting policy thus adapts online to realizations of uncertainty and provably compares well with the best obstacle avoidance policy in hindsight from a rich class of policies. The method is validated in simulation on a dynamical system environment and compared to baseline open-loop planning and robust Hamilton-Jacobi reachability techniques.

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