为扩散策略设计安全过滤机制，让机器人在保证安全的同时完成复杂任务。
From Demonstrations to Safe Deployment: Path-Consistent Safety Filtering for Diffusion Policies
- 通过路径一致性刹车，保持动作序列与训练分布一致
- 在动态环境中实现形式化安全保证，任务成功率不下降
- 相比传统方法提升68%任务成功率，适合真实人机交互场景
扩散策略（DPs）通过大规模示范数据学习，在复杂操作任务中表现优异，涵盖多种机械臂和环境。然而，它们无法保证行为安全，需依赖外部安全机制。这些机制会改变训练中未见的动作，导致不可预测行为和性能下降。为此，本文提出路径一致性安全过滤（PACS），对生成动作序列的轨迹进行路径一致性制动，确保执行过程与策略训练分布一致，维持任务完成能力。为实现实时部署并处理不确定性，采用基于集合的可达性分析验证安全性。在仿真及三个真实世界人机交互任务中的实验表明，PACS（a）在动态环境中提供形式化安全保证，（b）保持任务成功率，（c）相比控制屏障函数等反应式方法，任务成功率最高提升68%。视频展示见项目网站：https://tum-lsy.github.io/pacs。
原文摘要 · Abstract (English)
Diffusion policies (DPs) achieve state-of-the-art performance on complex manipulation tasks by learning from large-scale demonstration datasets, often spanning multiple embodiments and environments. However, they cannot guarantee safe behavior, requiring external safety mechanisms. These, however, alter actions in ways unseen during training, causing unpredictable behavior and performance degradation. To address these problems, we propose path-consistent safety filtering (PACS) for DPs. Our approach performs path-consistent braking on a trajectory computed from the sequence of generated actions. In this way, we keep the execution consistent with the training distribution of the policy, maintaining the learned, task-completing behavior. To enable real-time deployment and handle uncertainties, we verify safety using set-based reachability analysis. Our experimental evaluation in simulation and on three challenging real-world human-robot interaction tasks shows that PACS (a) provides formal safety guarantees in dynamic environments, (b) preserves task success rates, and (c) outperforms reactive safety approaches, such as control barrier functions, by up to 68 % in terms of task success. Videos are available at our project website: https://tum-lsy.github.io/pacs.
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