# Online Learning for Obstacle Avoidance

arXiv: https://arxiv.org/abs/2306.08776 · HTML: https://mlsanity.com/paper/2306.08776/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2023.

## Built on by (most cited first, 3 of 3)

- [Predictive Linear Online Tracking for Unknown Targets](https://mlsanity.com/paper/2402.10036/) (2024)
- [Universal Dynamic Regret and Constraint Violation Bounds for Constrained Online Convex Optimization](https://mlsanity.com/paper/2510.01867/) (2025)
- [TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking](https://mlsanity.com/paper/2609.07738/) (2026)

## Abstract (arXiv)

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 realizations of uncertainty in the environment or a stationary stochastic model of uncertainty, we propose a method that is efficient to implement and provably grants instance-optimality with respect to perturbations of trajectories generated from an open-loop planner (in the sense of minimizing worst-case regret). The resulting policy 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. Further, it is implemented on a hardware example where a quadruped robot traverses a dense obstacle field and encounters input disturbances due to time delays, model uncertainty, and dynamics nonlinearities.