ロボットの敵対的シナリオベース安全テスト：比較可能性と最適な攻撃性
On the Adversarial Scenario-based Safety Testing of Robots: the Comparability and Optimal Aggressiveness
ブラックボックス安全テストアルゴリズムの比較可能性と攻撃性を理論的に分析し、ほぼ安全集合に基づく攻撃性定義と効率的な比較アルゴリズムを提案。二足歩行制御と車両意思決定モジュールの実験で検証。
著者: Bowen Weng, Guillermo A. Castillo, Wei Zhang, Ayonga Hereid
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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 Moreover, for testing algorithms covering different sets of state-actions, the sampling efficiency criterion is no longer applicable as different algorithms do not necessarily converge to the same termination condition.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.
