外观
论文arXiv 2610.0540724 天前
TUCO：面向 Sim-to-Real 机器人策略协同训练的仿真示范筛选
原文链接：TUCO: Curating Simulation Demonstrations for Sim-to-Real Robot Policy Co-Training（arXiv 2610.05407 · 发布日期：2026-10-04）
TUCO: Curating Simulation Demonstrations for Sim-to-Real Robot Policy Co-Training
针对真实数据稀缺下的策略协同训练，TUCO用影响函数筛选仿真示范，并在RoboMimic和OmniReset验证。
核心要点
- 仿真示范可补充稀缺真实数据，但其主动筛选价值尚未充分研究。
- TUCO用影响函数追踪源示范对目标域评分rollout的影响。
- 方法结合轨迹效用与集合覆盖，减少冗余并选择互补示范。
- 实验显示TUCO在单仿真器、sim-to-sim和sim-to-real设置中达到SOTA性能。
验证范围与局限：验证范围为RoboMimic和OmniReset上的单仿真器、sim-to-sim与sim-to-real设置。
英文摘要 (Abstract)
Simulation demonstrations can supplement scarce real-world data for robot policy co-training. However, the value of using data curation to actively select these demonstrations for sim-to-real co-training remains underexplored. Existing curation methods also lack a unified criterion for measuring trajectory-level utility and set-level coverage from closed-loop target behavior. To address these gaps, we present the first systematic study of data curation for sim-to-real robot policy co-training and propose Trajectory-level Utility and set-level Coverage Optimization (TUCO). TUCO uses influence functions to trace how each source demonstration affects target-domain scoring rollouts. Our key insight is that these effects can be decomposed into an overall contribution to target return and variation across rollouts, providing a common closed-loop basis for measuring trajectory utility and set coverage. We further propose a performance-aligned subset optimizer that combines these measures in a unified curation objective to reduce redundancy and select complementary demonstrations. Extensive experiments on RoboMimic and OmniReset establish the value of active simulation data curation for sim-to-real policy co-training and show that TUCO achieves state-of-the-art performance across single-simulator, sim-to-sim, and sim-to-real settings.
作者：Ning Zhu, Mengfei Zhao, Yikai Tang, Zhangyujie Sun, Peihao Li, Dongyue Ni, Jindou Jia, Jianfei Yang
来源
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