Robots are more capable of achieving manipulation tasks for everyday activities than before. But the safety of manipulation skills that robots employ is still an open problem. Considering all possible failures during skill learning increases the complexity of the process and restrains learning an optimal policy. Beyond that, in unstructured environments, it is not easy to enumerate all possible failures beforehand. In the context of safe skill manipulation, we reformulate skills as base and failure prevention skills where base skills aim at completing tasks and failure prevention skills focus on reducing the risk of failures to occur. Then, we propose a modular and hierarchical method for safe robot manipulation by augmenting base skills by learning failure prevention skills with reinforcement learning, forming a skill library to address different safety risks. Furthermore, a skill selection policy that considers estimated risks is used for the robot to select the best control policy for safe manipulation. Our experiments show that the proposed method achieves the given goal while ensuring safety by preventing failures. We also show that with the proposed method, skill learning is feasible, novel failures are easily adaptable, and our safe manipulation tools can be transferred to the real environment.

翻译：机器人比以往更具执行日常活动操作任务的能力，但它们采用的操作技能的安全性仍是一个未解决的问题。在技能学习过程中考虑所有可能的故障会增加过程的复杂性，并限制最优策略的学习。此外，在非结构化环境中，难以事先列举所有可能的故障。在安全技能操作背景下，我们将技能重新定义为基本技能和故障预防技能，其中基本技能旨在完成任务，而故障预防技能侧重于降低故障发生的风险。然后，我们提出了一种模块化和层次化的安全机器人操作方法，通过强化学习学习故障预防技能来增强基本技能，形成一个技能库以应对不同的安全风险。此外，采用考虑估计风险的技能选择策略，使机器人能够选择最佳的控制策略以实现安全操作。我们的实验表明，所提出的方法在确保安全（通过防止故障）的同时实现了给定目标。我们还证明，使用所提出的方法，技能学习是可行的，新故障易于自适应，并且我们的安全操作工具可以转移到真实环境中。