Dynamic Obstacle Avoidance through Uncertainty-Based Adaptive Planning with Diffusion

TL;DR

This paper introduces an adaptive planning method using diffusion models that dynamically adjusts replanning frequency based on uncertainty, improving obstacle avoidance efficiency and safety in dynamic environments.

Contribution

It presents a novel adaptive generative planning approach that reduces computational overhead while maintaining robust collision avoidance in dynamic scenarios.

Findings

13.5% longer mean trajectory length

12.7% higher mean reward

Reduced collision rates

Abstract

By framing reinforcement learning as a sequence modeling problem, recent work has enabled the use of generative models, such as diffusion models, for planning. While these models are effective in predicting long-horizon state trajectories in deterministic environments, they face challenges in dynamic settings with moving obstacles. Effective collision avoidance demands continuous monitoring and adaptive decision-making. While replanning at every timestep could ensure safety, it introduces substantial computational overhead due to the repetitive prediction of overlapping state sequences -- a process that is particularly costly with diffusion models, known for their intensive iterative sampling procedure. We propose an adaptive generative planning approach that dynamically adjusts replanning frequency based on the uncertainty of action predictions. Our method minimizes the need for frequent, computationally expensive, and redundant replanning while maintaining robust collision avoidance performance. In experiments, we obtain a 13.5% increase in the mean trajectory length and a 12.7% increase in mean reward over long-horizon planning, indicating a reduction in collision rates and an improved ability to navigate the environment safely.