论文/arXiv 2610.17521·15 天前

YUBI-Real2Sim：用于可靠策略评估的 3DGS 场景验证

针对真机评估 VLA 成本高且难复现的问题，YUBI-Real2Sim 用 3DGS 重建与 MuJoCo 仿真，在双鱼眼腕视角下验证策略评估。

AI 整理

• 仿真与训练
• 数据与采集
• 具身模型

原文链接：YUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation（arXiv 2610.17521 · 发布日期：2026-10-13）

YUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation

针对真机评估 VLA 成本高且难复现的问题，YUBI-Real2Sim 用 3DGS 重建与 MuJoCo 仿真，在双鱼眼腕视角下验证策略评估。

核心要点

• 真机评估 VLA 策略成本高、耗时且难复现，3DGS 重建质量对可靠评估的要求尚未量化。
• 框架结合 200 度鱼眼采集、3DGS 训练、3D foundation models 物体建模与 MuJoCo 物理仿真。
• 在两个双臂机器人平台、三场景、三任务和双 pi0.5-based 策略上构建基准，八个九个场景任务比较中复现真机策略排序。
• 系统评估 3DGS 伪影与采集扰动影响，并分析新视角质量分数对策略排序可靠性的指示能力。

验证范围与局限：验证范围为两个双臂机器人平台、三个场景、三个任务和双 pi0.5-based 策略，尚未建立通用可靠的场景接受规则。

英文摘要 (Abstract)

Evaluating VLA policies on real robots is costly, time-consuming, and difficult to reproduce, making real-to-sim simulation essential for scalable evaluation. While 3D Gaussian Splatting (3DGS) enables rapid real-to-sim scene generation, the reconstruction quality required for reliable policy evaluation remains unquantified. In particular, policies relying on wrist-mounted fisheye cameras (e.g., YUBI handheld gripper data) are heavily exposed to visual artifacts (holes, floaters) in wide-angle views, directly degrading task performance. To address this, we present YUBI-Real2Sim, a unified framework for 3DGS-based scene reconstruction and real-to-sim policy evaluation under dual fisheye wrist views. We develop an integrated pipeline combining 200-degree fisheye camera capture, 3DGS training, 3D foundation models for object modeling, and MuJoCo-based physical simulation, reproducing the observed real-world policy ordering in eight of nine scene-task comparisons. Using this framework, we construct a benchmark across two bimanual robot platforms, three scenes, three tasks, and dual pi0.5-based policies. This systematically measures real-to-sim rank agreement and evaluates policy sensitivity against counterfactual 3DGS artifacts (ceiling holes, floaters, warping) and capture disturbances. We further analyze whether novel-view quality scores can indicate policy-ranking reliability under a scene-held-out evaluation protocol. The results show task-dependent performance and limitations of quality-based reliability prediction, highlighting the need to assess reconstruction quality when using Real2Sim for policy evaluation. These results do not yet establish a generally reliable scene-acceptance rule. Project Page: https://yubi-real2sim.airoa.io.

作者：Hiroki Nishizawa, Takehiko Ohkawa, Nicola Scianca, Tatsuya Matsushima, Shigeo Morishima, Kei Ota

来源

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1. arXiv 2610.17521：YUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation访问 2026-10-21