AI Finds AI Flaws - Surrogate Assisted Generation of Human-Robot Interaction Scenarios

Описание: https://arxiv.org/abs/2304.13787v2
https://proceedings.mlr.press/v229/bh...
Surrogate Assisted Generation of Human-Robot Interaction Scenarios
CoRL 2023

Abstract:
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.

Authors:
Varun Bhatt, Heramb Nemlekar, Matthew C. Fontaine, Bryon Tjanaka, Hejia Zhang, Ya-Chuan Hsu, Stefanos Nikolaidis


Keywords:
Human-Robot Interaction (HRI)
Scenario Generation
Surrogate Modeling
Shared Control
Teleoperation
Quality Diversity
Failure Analysis
Robot Simulation
Human-Robot Collaboration