不確実性に基づく適応的計画による動的障害物回避と拡散モデル
Dynamic Obstacle Avoidance through Uncertainty-Based Adaptive Planning with Diffusion
拡散モデルを用いた計画において、行動予測の不確実性に応じて再計画の頻度を適応的に調整し、計算コストを抑えつつ動的障害物回避の性能を向上させる手法を提案した。
著者: Vineet Punyamoorty, Pascal Jutras-Dubé, Ruqi Zhang, Vaneet Aggarwal, Damon Conover, Aniket Bera
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.
