Surrogate Assisted Generation of Human-Robot Interaction Scenarios
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 51 of its 76 references that are in the map. First posted on arXiv 26 Apr 2023.
Builds on
51 of 76 references.
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Citing papers per year: 2023: 1; 2024: 4; 2025: 1; 2026: 2.
8 papers.
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