从演示到安全部署：扩散策略的路径一致安全过滤
From Demonstrations to Safe Deployment: Path-Consistent Safety Filtering for Diffusion Policies
AI总结：
本文提出路径一致安全过滤方法，用于提升扩散策略在动态环境中的安全性和任务成功率。
摘要：
扩散策略（DPs）通过从大规模演示数据集中学习，在复杂操作任务中实现了最先进的性能，通常涵盖多个实体和环境。然而，它们无法保证安全行为，需要外部安全机制。然而，这些机制以训练期间未见过的方式修改动作，导致不可预测的行为和性能下降。为了解决这些问题，我们提出了适用于DPs的路径一致安全过滤（PACS）。我们的方法在由生成动作序列计算出的轨迹上执行路径一致的制动。这样，我们保持执行与策略的训练分布一致，维持学习到的任务完成行为。为了实现实时部署并处理不确定性，我们使用基于集合的可达性分析来验证安全性。我们在模拟环境和三个具有挑战性的现实世界人机交互任务中的实验评估表明，PACS（a）在动态环境中提供正式的安全保证，（b）保持任务成功率，（c）在任务成功率方面比反应式安全方法，如控制屏障函数，高出多达68%。视频可在我们的项目网站上查看：https://tum-lsy.github.io/pacs。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
