Robo-Saber: Generating and Simulating Virtual Reality Players

Nam Hee Kim; Jingjing May Liu; Jaakko Lehtinen; Perttu Hämäläinen; James F. O'Brien; Xue Bin Peng

Robo-Saber: Generating and Simulating Virtual Reality Players

Graphics 2026-02-23 v1 Artificial Intelligence Human-Computer Interaction Machine Learning

Authors: Nam Hee Kim , Jingjing May Liu , Jaakko Lehtinen , Perttu Hämäläinen , James F. O'Brien , Xue Bin Peng

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.

Cite

@article{arxiv.2602.18319,
  title  = {Robo-Saber: Generating and Simulating Virtual Reality Players},
  author = {Nam Hee Kim and Jingjing May Liu and Jaakko Lehtinen and Perttu Hämäläinen and James F. O'Brien and Xue Bin Peng},
  journal= {arXiv preprint arXiv:2602.18319},
  year   = {2026}
}

Comments

13 pages, 15 figures. Accepted to Eurographics 2026. Project page: https://robo-saber.github.io/

Robo-Saber: Generating and Simulating Virtual Reality Players

Abstract

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