Surrogate Assisted Generation of Human-Robot Interaction Scenarios

Varun Bhatt, Heramb Nemlekar, Matthew Christopher Fontaine, Bryon Tjanaka, Hejia Zhang, Ya-Chuan Hsu, Stefanos Nikolaidis

Keywords: Scenario Generation, Human-Robot Interaction, Quality Diversity

TL;DR: We present an efficient method of automatically generating challenging and diverse human-robot interaction scenarios.

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.

Student First Author: yes

Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)

Video: https://drive.google.com/drive/folders/1W1627W26ManC_iY8G3HAJcPxhj8-mZG2

Code: https://github.com/icaros-usc/dsas

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/surrogate-assisted-generation-of-human-robot/code)