用可达性分析保障模仿学习的机器人安全执行，避免违规行为。
RAIL: Reachability-Aided Imitation Learning for Safe Policy Execution
- 基于可达性构建安全过滤器，强制执行硬约束
- 高性能策略常因违规而表现好，加约束后性能下降
- 低性能策略加约束后反而更安全完成任务
模仿学习（IL）在复杂机器人操作任务中已取得显著成功。然而，仍需实用的安全方法以支持其广泛部署。尤其在不允许通过调参权衡性能与安全（即使用软约束）的情况下，必须确保系统严格遵守对不安全行为的硬约束。这引出一个问题：强制执行硬约束如何影响IL策略的性能（即安全完成任务的能力）？为此，本文构建了一种基于可达性的安全过滤器，用于在模仿学习中施加硬约束，称为可达性辅助模仿学习（RAIL）。通过对移动机器人和操作任务中的先进IL策略进行评估，发现两个关键结果：第一，表现最佳的策略有时仅因频繁违反约束而获得高分，但在强约束下性能显著下降；第二，令人意外的是，对表现较差的策略施加硬约束反而可能提升其安全完成任务的能力。最后，硬件实验验证了该方法可实时运行。
原文摘要 · Abstract (English)
Imitation learning (IL) has shown great success in learning complex robot manipulation tasks. However, there remains a need for practical safety methods to justify widespread deployment. In particular, it is important to certify that a system obeys hard constraints on unsafe behavior in settings when it is unacceptable to design a tradeoff between performance and safety via tuning the policy (i.e. soft constraints). This leads to the question, how does enforcing hard constraints impact the performance (meaning safely completing tasks) of an IL policy? To answer this question, this paper builds a reachability-based safety filter to enforce hard constraints on IL, which we call Reachability-Aided Imitation Learning (RAIL). Through evaluations with state-of-the-art IL policies in mobile robots and manipulation tasks, we make two key findings. First, the highest-performing policies are sometimes only so because they frequently violate constraints, and significantly lose performance under hard constraints. Second, surprisingly, hard constraints on the lower-performing policies can occasionally increase their ability to perform tasks safely. Finally, hardware evaluation confirms the method can operate in real time.
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