拡散モデルによる自律移動計画のための非微分可能報酬最適化
Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning
拡散モデルを用いた移動計画において、衝突回避や目標到達といった微分不可能な目的を強化学習で直接最適化する訓練手法を提案し、歩行者データセットで最先端性能を達成した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Giwon Lee, Daehee Park, Jaewoo Jeong, Kuk-Jin Yoon
分類: cs.RO
原文アブストラクト
Safe and effective motion planning is crucial for autonomous robots. Diffusion models excel at capturing complex agent interactions, a fundamental aspect of decision-making in dynamic environments. Recent studies have successfully applied diffusion models to motion planning, demonstrating their competence in handling complex scenarios and accurately predicting multi-modal future trajectories. Despite their effectiveness, diffusion models have limitations in training objectives, as they approximate data distributions rather than explicitly capturing the underlying decision-making dynamics. However, the crux of motion planning lies in non-differentiable downstream objectives, such as safety (collision avoidance) and effectiveness (goal-reaching), which conventional learning algorithms cannot directly optimize. In this paper, we propose a reinforcement learning-based training scheme for diffusion motion planning models, enabling them to effectively learn non-differentiable objectives that explicitly measure safety and effectiveness. Specifically, we introduce a reward-weighted dynamic thresholding algorithm to shape a dense reward signal, facilitating more effective training and outperforming models trained with differentiable objectives. State-of-the-art performance on pedestrian datasets (CrowdNav, ETH-UCY) compared to various baselines demonstrates the versatility of our approach for safe and effective motion planning.
