Robo-Saber: Generating and Simulating Virtual Reality Players

Conference of the European Association for Computer Graphics (Eurographics 2026)

Nam Hee Kim (1)    Jingjing May Liu (2)    Jaakko Lehtinen (1, 3)    Perttu Hämäläinen (1)    James F. O'Brien 2)    Xue Bin Peng (3, 4)

(1) Aalto University    (2) University of California, Berkeley    (3) NVIDIA    (4) Simon Fraser University

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Abstract

We present the first motion generation system for playtesting virtual reality (VR) games. Our player model generates VR headset and handheld controller movements from in-game object arrangements, guided by style exemplars and aligned to maximize simulated gameplay score. We train on the large BOXRR-23 dataset and apply our framework on the popular VR game Beat Saber. The resulting model Robo-Saber produces skilled gameplay and captures diverse player behaviors, mirroring the skill levels and movement patterns specified by input style exemplars. Robo-Saber demonstrates promise in synthesizing rich gameplay data for predictive applications and enabling a physics-based whole-body VR playtesting agent

Paper: [PDF]       Webpage: [Link]       Preprint: [arXiv]

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Bibtex

@inproceedings{
	kim2026robo,
	title={Robo-Saber: Generating and Simulating Virtual Reality Players},
	author={Kim, Nam Hee and Liu, Jingjing May and Lehtinen, Jaakko and H{\"a}m{\"a}l{\"a}inen, Perttu and O'Brien, James and Peng, Xue Bin}, booktitle={arXiv preprint},
	year={2026}
}