On the Adversarial Scenario-based Safety Testing of Robots: the Comparability and Optimal Aggressiveness

TL;DR

This paper challenges conventional beliefs in scenario-based safety testing of robots, proving that different sampling strategies perform equally well under certain conditions and introducing a new measure of testing aggressiveness.

Contribution

It introduces an impossibility theorem for safety testing algorithms with the same coverage but different sampling distributions and proposes a new aggressiveness metric with an efficient comparison algorithm.

Findings

All algorithms with the same coverage perform equally in sampling efficiency.

Sampling efficiency is not comparable across algorithms with different coverage.

Empirical results support the theoretical findings on robotic safety testing.

Abstract

This paper studies the class of scenario-based safety testing algorithms in the black-box safety testing configuration. For algorithms sharing the same state-action set coverage with different sampling distributions, it is commonly believed that prioritizing the exploration of high-risk state-actions leads to a better sampling efficiency. Our proposal disputes the above intuition by introducing an impossibility theorem that provably shows all safety testing algorithms of the aforementioned difference perform equally well with the same expected sampling efficiency. Moreover, for testing algorithms covering different sets of state-actions, the sampling efficiency criterion is no longer applicable as different algorithms do not necessarily converge to the same termination condition. We then propose a testing aggressiveness definition based on the almost safe set concept along with an unbiased and efficient algorithm that compares the aggressiveness between testing algorithms. Empirical observations from the safety testing of bipedal locomotion controllers and vehicle decision-making modules are also presented to support the proposed theoretical implications and methodologies.