用扩散模型生成机器人避障动作块，提升密集人群中的导航成功率。
Diffusion Policies for Short-Horizon Planning in Robot Crowd Navigation
- 用扩散模型生成5步动作块，替代传统单步决策。
- 在受限边界环境下，成功率显著优于现有方法。
- 适合需要灵活短时规划的机器人导航任务。
机器人在密集动态的人群中导航需安全高效的决策能力。现有强化学习方法通常每步输出单一反应动作，难以表达多样的短期避障策略。本文提出离线预训练结合在线微调的规划扩散策略优化（PDPO）框架，利用扩散策略生成短时行动块。该方法先在避障演示数据上预训练，再通过PPO在线微调，将去噪过程视为内部决策机制。执行时，策略生成五步动作块，并以滚动时域方式应用。此外，我们发现常见基准存在评估偏差：缺乏边界约束时，智能体可能脱离有效区域绕过人群。为此，我们引入新设定，将越界视为碰撞。实验表明，PDPO在多个基准上优于强基线，消融实验证明动作块对改进后的有界基准尤为关键。
原文摘要 · Abstract (English)
Robot crowd navigation requires safe and efficient decision-making under dense, dynamic, and multimodal human--robot interactions. Existing reinforcement-learning methods typically output a single reactive action at each timestep, which limits their ability to represent diverse short-term avoidance strategies. We propose Planning Diffusion Policy Optimization (PDPO), an offline-to-online reinforcement-learning framework that uses a diffusion policy to generate short-horizon action chunks for crowd navigation. PDPO is first pretrained on collision-avoidance demonstrations and then fine-tuned online with PPO by treating the denoising process as an internal decision process. During execution, the policy generates a five-step action chunk and applies it in a receding-horizon manner. Furthermore, we observe an evaluation artifact in common crowd-navigation benchmarks: without explicit boundary constraints, learned agents may leave the valid domain and bypass dense crowds. To address this, we introduce a setting in which boundary violations are treated as collisions. Experiments show that PDPO obtains an improved success rate over strong baselines, and ablations demonstrate that action chunks are especially important for the modified bounded benchmark.
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