Simulation data selection improves robot training for real tasks
TUCO: Curating Simulation Demonstrations for Sim-to-Real Robot Policy Co-Training
Robotics
Summary
Robots need lots of practice data to learn how to do tasks well, but real-world data is often scarce and expensive. The authors show that carefully choosing which simulated practice runs to include helps robots learn more effectively when combining simulated and real data. Their new method, called TUCO, picks useful and diverse simulation examples to improve robot learning. Tests show that this approach boosts performance in different robot training scenarios.
What this means in practice
- •For robotics engineers: Train robot control policies more efficiently by selecting helpful simulated examples to combine with limited real-world data.
- •For simulation software developers: Improve training toolkits by integrating data curation methods that select the most beneficial simulation runs for robot learning.
Authors
Ning Zhu, Mengfei Zhao, Yikai Tang, Zhangyujie Sun, Peihao Li, Dongyue Ni, Jindou Jia, Jianfei Yang
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.