2023-12-02-bhatt23a.md

title	section	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Surrogate Assisted Generation of Human-Robot Interaction Scenarios	Oral	C5MQUlzhVjQ	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bhatt23a	0	Surrogate Assisted Generation of Human-Robot Interaction Scenarios	513	539	513-539	513	false	Bhatt, Varun and Nemlekar, Heramb and Fontaine, Matthew Christopher and Tjanaka, Bryon and Zhang, Hejia and Hsu, Ya-Chuan and Nikolaidis, Stefanos	given family Varun Bhatt given family Heramb Nemlekar given family Matthew Christopher Fontaine given family Bryon Tjanaka given family Hejia Zhang given family Ya-Chuan Hsu given family Stefanos Nikolaidis	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/bhatt23a/bhatt23a.pdf