用仿真和真实数据联合训练，让机器人推物任务更高效
Empirical Analysis of Sim-and-Real Cotraining of Diffusion Policies for Planar Pushing from Pixels
- 用仿真与真实数据协同训练，提升策略性能
- 真实数据少时，仿真数据越多越有效，有上限
- 适度视觉差异反而助于区分真假，利于迁移
模拟与真实数据协同训练已成为规模化机器人模仿学习的有力方法。本研究深入剖析这一策略的基本原理，旨在指导仿真设计、数据集构建与策略训练。实验表明，使用仿真数据可显著提升性能，尤其在真实数据有限时；性能随仿真数据增加而上升，直至达到平台期；增加真实数据可提高该平台高度。结果还显示，对于非抓取或接触密集型任务，缩小物理域差距比提升视觉保真度更具影响。令人意外的是，部分视觉差异有助于协同训练——二值探测表明高性能策略需学会区分仿真与真实环境。最后，我们探讨了促进正向迁移的机制。聚焦于从像素出发的平面推物任务，共完成50余项真实策略测试（1000+次试验）及250项仿真策略测试（5万+次试验）。视频与代码见https://sim-and-real-cotraining.github.io/。
原文摘要 · Abstract (English)
Cotraining with demonstration data generated both in simulation and on real hardware has emerged as a promising recipe for scaling imitation learning in robotics. This work seeks to elucidate basic principles of this sim-and-real cotraining to inform simulation design, sim-and-real dataset creation, and policy training. Our experiments confirm that cotraining with simulated data can dramatically improve performance, especially when real data is limited. We show that these performance gains scale with additional simulated data up to a plateau; adding more real-world data increases this performance ceiling. The results also suggest that reducing physical domain gaps may be more impactful than visual fidelity for non-prehensile or contact-rich tasks. Perhaps surprisingly, we find that some visual gap can help cotraining -- binary probes reveal that high-performing policies must learn to distinguish simulated domains from real. We conclude by investigating this nuance and mechanisms that facilitate positive transfer between sim-and-real. Focusing narrowly on the canonical task of planar pushing from pixels allows us to be thorough in our study. In total, our experiments span 50+ real-world policies (evaluated on 1000+ trials) and 250 simulated policies (evaluated on 50,000+ trials). Videos and code can be found at https://sim-and-real-cotraining.github.io/.
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