做或不做：确保从示范中学习的视觉-运动策略的安全性
To Do or Not to Do: Ensuring the Safety of Visuomotor Policies Learned from Demonstrations
AI总结：
本文提出了一种政策无关的安全度量，确保视觉-运动策略在状态空间特定区域内的最大任务成功率，通过视图合成和Nagumo子切线条件分析，验证了策略安全性和性能的平衡。
摘要：
任务成功率历来是模仿学习（IL）研究中政策性能的主要衡量标准。这一特点严格限制了IL算法在田间机器人中的广泛应用，因为在这些环境中，安全保证与任务成功率同样重要。在实际应用中，IL驱动的机器人不应执行策略并导致表现不佳，如果安全无法保证。尽管在经典控制文献中，安全与性能之间的权衡已被广泛研究，但政策安全在IL研究中仍是一个严重缺乏探索的领域。没有通用的安全定义。更糟糕的是，许多现有的安全理论工作难以扩展到田间IL驱动的机器人。本文提供了关于IL策略安全性和性能的重要见解。我们提出了一种执行保证，一种政策无关的安全度量，它确保了视觉-运动IL策略在状态空间特定区域内的最大任务成功率，即使在运行时有微小的变化。我们利用最近的视图合成进展来识别状态空间中的此类区域，并探索集合不变性的一个基本结果——即Nagumo的子切线条件——以证明并操作化执行保证。使用Franka机器人在模拟和现实世界中的实验展示了所提出的安全分析如何允许各种IL策略在保证下实现最大任务成功率。我们还展示了恢复策略——由所提出的安全分析产生的副产品——如何帮助提高策略性能，从而缓解IL中的安全-性能权衡。
英文摘要：
Task success has historically been the primary measure of policy performance in imitation learning (IL) research. This characteristics strictly limits the ubiquitous applications of IL algorithms in field robotics where safety assurance, in addition to task-success, is of paramount importance. It is often desirable for an IL-powered robot in the field not to roll out a policy, and hence score a poor performance, if the safety is not guaranteed. Although this trade-off between safety and performance is well investigated in classical control literature, policy safety is a heavily underexplored domain in IL research. There is no universal definition of safety in IL. To make things worst, many existing theoretical works on safety is notoriously difficult to extend to IL-powered robots in the field. This paper offers important insights on the safety and performance of IL policies. We propose execution guarantee, a policy-agnostic safety measure that guarantees the maximum task success for a visuomotor IL policy, despite minor run-time changes, from within a specific region in the state space. We leverage recent advances in view synthesis to identify such regions in the state space for an IL policy and explore a fundamental result on set invariance - namely, Nagumo's sub-tangentiality condition - to prove and operationalize execution guarantee from inside that region. Experiments with a Franka robot, both in simulation and real world, demonstrate how the proposed safety analysis allows various IL policies to achieve maximum task success with guarantee. We also demonstrate some interesting results on how a recovery policy - a by-product of the proposed safety analysis - can help to increase the policy performance and thereby mitigating the safety-performance tradeoff in IL.
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。
