The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring the diverse scenarios of interaction between humans and robots in simulation can improve understanding of complex human-robot interaction systems and avoid potentially costly failures in real-world settings.
In this talk, I propose formulating the problem of automatic scenario generation in human-robot interaction as a quality diversity problem, where the goal is not to find a single global optimum, but a diverse range of failure scenarios that explore both environments and human actions. I show how standard quality diversity algorithms can discover surprising and unexpected failure cases in the shared autonomy domain. I then discuss the development of a new class of quality diversity algorithms that significantly improve the search of the scenario space and the integration of these algorithms with generative models, which enables the generation of complex and realistic scenarios. Finally, I discuss applications in procedural content generation and human preference learning.