Risk assessment of a robot in controlled environments, such as laboratories and proving grounds, is a common means to assess, certify, validate, verify, and characterize the robots' safety performance before, during, and even after their commercialization in the real-world. A standard testing program that acquires the risk estimate is expected to be (i) repeatable, such that it obtains similar risk assessments of the same testing subject among multiple trials or attempts with the similar testing effort by different stakeholders, and (ii) reliable against a variety of testing subjects produced by different vendors and manufacturers. Both repeatability and reliability are fundamental and crucial for a testing algorithm's validity, fairness, and practical feasibility, especially for standardization. However, these properties are rarely satisfied or ensured, especially as the subject robots become more complex, uncertain, and varied. This issue was present in traditional risk assessments through Monte-Carlo sampling, and remains a bottleneck for the recent accelerated risk assessment methods, primarily those using importance sampling. This study aims to enhance existing accelerated testing frameworks by proposing a new algorithm that provably integrates repeatability and reliability with the already established formality and efficiency. It also features demonstrations assessing the risk of instability from frontal impacts, initiated by push-over disturbances on a controlled inverted pendulum and a 7-DoF planar bipedal robot Rabbit managed by various control algorithms.

翻译：在实验室和试验场等受控环境中对机器人进行风险评估，是评估、认证、验证、确认和表征机器人在真实世界商业化之前、期间甚至之后安全性能的常用手段。一个获取风险估计的标准测试程序应具备以下特性：(i) 可重复性，即不同利益相关方在付出相似测试努力的情况下，对同一测试对象进行多次试验或尝试时能获得相似的风险评估结果；(ii) 可靠性，即能可靠地应对不同供应商和制造商生产的不同测试对象。可重复性和可靠性对于测试算法的有效性、公平性和实际可行性至关重要，尤其是在标准化方面。然而，这些特性很少得到满足或保证，特别是当被测机器人变得更加复杂、不确定和多样化时。这一问题在通过蒙特卡洛采样的传统风险评估中就已存在，并且仍然是近期加速风险评估方法（主要是那些使用重要性采样的方法）的瓶颈。本研究旨在通过提出一种新算法来增强现有的加速测试框架，该算法可证明地将可重复性和可靠性与已建立的形式化及高效性相结合。研究还展示了在受控倒立摆和由不同控制算法管理的7自由度平面双足机器人Rabbit上，评估由前向推覆扰动引发的失稳风险的演示案例。