  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.
