R2S-EGO：面向稀疏捕获虚实迁移的双代理细化方法
R2S-EGO: Dual-Proxy Refinement for Sparse-Capture Real-to-Sim
- XPENG Robotics（小鹏机器人）
- The Hong Kong Polytechnic University（香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
R2S-EGO耦合机器人与几何双代理，通过固定预算选择细化视觉资产，在Replica场景实验中，其PSNR与G1坐立成功率均显著优于现有R2S基线。
AI中文摘要：
虚实迁移（Real-to-sim，R2S）依赖于场景表示，该表示会渲染机器人自身轨迹上的观测结果，但密集多视图捕获会限制每个环境的真实图像捕获计数效率，而稀疏人工捕获会导致行为范围内的机器人视图支持不足。相机控制合成可填补缺失视图，但其在R2S中的应用需要符合行为的查询以及以捕获为锚点的结构条件。我们提出R2S-EGO，它将模拟器衍生的机器人代理（表示行为范围内的可执行查询域）与以捕获为锚点的几何代理（提供场景特定的结构条件）相结合。在该域内，固定预算选择针对当前存在几何支持的支持不足区域。生成的观测结果被作为伪观测结果同化，以细化视觉资产，而真实捕获仍作为锚点。融合后的几何代理还提供场景碰撞表面，该表面会在轮次之间刷新。这些更新共同细化了现有仿真场景，而其机器人动力学和控制栈保持固定。在三个Replica场景的48个冻结Unitree G1自身视图上，六视图R2S-EGO达到19.062 dB的峰值信噪比（PSNR），相比之下，已报道的最强R2S基线为14.226 dB。在五个配对的策略训练随机种子上，R2S-EGO实现了82.5%±6.8%的真实G1坐立成功率，而GaussGym为10.0%±10.5%。
英文摘要：
Real-to-sim (R2S) depends on scene representations that render observations along robot ego trajectories, yet dense multi-view capture limits per-environment real-image capture-count efficiency, and sparse human capture can leave behavior-scoped robot views under-supported. Camera-controlled synthesis can fill missing views, but its use in R2S requires behavior-admissible queries and capture-anchored structural conditioning. We present R2S-EGO, which couples a simulator-derived robot proxy that represents the behavior-scoped executable query domain with a capture-anchored geometry proxy that supplies scene-specific structural conditions. Within this domain, fixed- budget selection targets current support deficits for which geometry support is available. The generated observations are assimilated as pseudo-observations to refine the visual asset, while real captures remain anchors. The fused geometry proxy also supplies the scene collision surface, which is refreshed between rounds. Together, these updates refine the existing simulation scene while its robot dynamics and control stack stay fixed. Across 48 frozen Unitree G1 ego views in three Replica scenes, six-view R2S-EGO reaches 19.062 dB PSNR, compared with 14.226 dB for the strongest reported R2S baseline. Across five paired policy-training seeds, R2S-EGO achieves 82.5% +/- 6.8% real-G1 sitting success, compared with 10.0% +/- 10.5% for GaussGym.
