Robotics: Science and Systems XXII

GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning

Yufei Jia, Heng Zhang, Ziheng Zhang, Lei Han, Junzhe Wu, Mingrui Yu, Zifan Wang, Dixuan Jiang, Zheng Li, Chenyu Cao, Zhuoyuan Yu, Xun Yang, Haizhou Ge, Yuchi Zhang, Jiayuan Zhang, Zhenbiao Huang, Tianle Liu, Shenyu Chen, Jiacheng Wang, Bin Xie, Xuran Yao, Xiwa Deng, Guangyu Wang, Jinzhi Zhang, Lei Hao, Zhixing Chen, Yuxiang Chen, Anqi Wang, Hongyun Tian, Yiyi Yan, Zhanxiang Cao, Yizhou Jiang, Hanyang Shao, Yue Li, Lu Shi, Bokui Chen, Wei Sui, Hanqing Cui, Yusen Qin, Tiancai Wang, Ruqi Huang, Guyue Zhou

Abstract:

Embodied AI research is undergoing a shift toward vision-centric perceptual paradigms. While massively parallel simulators have catalyzed breakthroughs in proprioception-based locomotion, their potential remains largely untapped for vision-centric tasks due to the prohibitive computational overhead of large-scale photorealistic rendering. Furthermore, the creation of simulation-ready 3D assets heavily relies on labor-intensive manual modeling, while the significant sim-to-real physical gap hinders the transfer of contact-rich manipulation policies. To address these bottlenecks, we propose gs_playground, a multi-modal simulation framework designed to accelerate end-to-end perceptual learning. We develop a novel high-performance parallel physics engine, specifically designed to integrate with a batch 3D Gaussian Splatting (3DGS) rendering pipeline to ensure high-fidelity synchronization. Our system achieves a breakthrough throughput of \mathbf{10^4} FPS at \mathbf{640 × 480} resolution, significantly lowering the barrier for large-scale visual RL. Additionally, we introduce an automated Real2Sim workflow that reconstructs photorealistic, physically consistent, and memory-efficient environments, streamlining the generation of complex simulation-ready scenes. Extensive experiments on locomotion, navigation, and manipulation demonstrate that gs_playground effectively bridges the perceptual and physical gaps across diverse embodied tasks. We will open-source the full-stack framework to empower the research community.

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Bibtex:

  
@INPROCEEDINGS{JiaY-RSS-26, 
    AUTHOR    = {Yufei Jia AND Heng Zhang AND Ziheng Zhang AND Lei Han AND Junzhe Wu AND Mingrui Yu AND Zifan Wang AND Dixuan Jiang AND Zheng Li AND Chenyu Cao AND Zhuoyuan Yu AND Xun Yang AND Haizhou Ge AND Yuchi Zhang AND Jiayuan Zhang AND Zhenbiao Huang AND Tianle Liu AND Shenyu Chen AND Jiacheng Wang AND Bin Xie AND Xuran Yao AND Xiwa Deng AND Guangyu Wang AND Jinzhi Zhang AND Lei Hao AND Zhixing Chen AND Yuxiang Chen AND Anqi Wang AND Hongyun Tian AND Yiyi Yan AND Zhanxiang Cao AND Yizhou Jiang AND Hanyang Shao AND Yue Li AND Lu Shi AND Bokui Chen AND Wei Sui AND Hanqing Cui AND Yusen Qin AND Tiancai Wang AND Ruqi Huang AND Guyue Zhou}, 
    TITLE     = {{GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.093} 
}