As human-robot interaction (HRI) systems advance, so does the difficulty of
evaluating and understanding the strengths and limitations of these systems in
different environments and with different users. To this end, previous methods
have algorithmically generated diverse scenarios that reveal system failures in
a shared control teleoperation task. However, these methods require directly
evaluating generated scenarios by simulating robot policies and human actions.
The computational cost of these evaluations limits their applicability in more
complex domains. Thus, we propose augmenting scenario generation systems with
surrogate models that predict both human and robot behaviors. In the shared
control teleoperation domain and a more complex shared workspace collaboration
task, we show that surrogate assisted scenario generation efficiently
synthesizes diverse datasets of challenging scenarios. We demonstrate that
these failures are reproducible in real-world interactions.

在人机交互系统中，结合代理模型的场景生成系统可以有效地合成多样而具有挑战性的场景，并且在真实世界的交互中可以重现这些失败情境。