Algorithmic Scenario Generation as Quality Diversity Optimization
This addresses the critical need for robust testing of robots and autonomous agents interacting with people, though it appears incremental as a review and framework integration.
The paper tackles the problem of systematically testing complex robots and autonomous agents before deployment by presenting a general framework for algorithmic scenario generation, which discovers diverse realistic and challenging scenarios that reveal previously unknown failures in deployed systems interacting with people.
The increasing complexity of robots and autonomous agents that interact with people highlights the critical need for approaches that systematically test them before deployment. This review paper presents a general framework for solving this problem, describes the insights that we have gained from working on each component of the framework, and shows how integrating these components leads to the discovery of a diverse range of realistic and challenging scenarios that reveal previously unknown failures in deployed robotic systems interacting with people.