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.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Optimization (eg, convex and non-convex optimization)

Community Implementations: [ 4 code implementations](https://www.catalyzex.com/paper/arxiv:2306.08776/code)