Robo-Saber: Generating and Simulating Virtual Reality Players

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.

13 pages, 15 figures. Accepted to Eurographics 2026. Project page: this https URL

Graphics (cs.GR); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)

arXiv:2602.18319 [cs.GR]

(or arXiv:2602.18319v2 [cs.GR] for this version)

https://doi.org/10.48550/arXiv.2602.18319 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1111/cgf.70353 Focus to learn more DOI(s) linking to related resources

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