像素平面推挤任务中仿真与真实联合训练扩散策略的实证分析
Empirical Analysis of Sim-and-Real Cotraining of Diffusion Policies for Planar Pushing from Pixels
AI总结：
本研究实证分析仿真与真实数据联合训练扩散策略，发现仿真数据可显著提升性能，且缩小物理域差距比视觉保真度更重要，并揭示一定视觉差异有助于策略区分域。
摘要：
利用仿真和真实硬件上生成的演示数据进行联合训练，已成为扩展机器人模仿学习规模的一种有前景的方法。本研究旨在阐明这种仿真与真实联合训练的基本原理，以指导仿真设计、仿真与真实数据集的创建以及策略训练。我们的实验证实，与仿真数据联合训练可以显著提升性能，尤其是在真实数据有限的情况下。我们表明，这些性能提升会随着额外仿真数据的增加而增长，直至达到一个平台期；增加更多真实世界数据会提高这一性能上限。结果还表明，对于非抓取或接触丰富的任务，缩小物理域差距可能比视觉保真度更具影响力。或许令人惊讶的是，我们发现一些视觉差距有助于联合训练——二元探针实验揭示，高性能策略必须学会区分仿真域与真实域。最后，我们研究了这一细微差别以及促进仿真与真实之间正向迁移的机制。将研究范围严格限定在像素平面推挤这一典型任务上，使我们能够进行深入细致的研究。总体而言，我们的实验涵盖了50多个真实世界策略（在1000多次试验中评估）和250个仿真策略（在50000多次试验中评估）。视频和代码可在https://sim-and-real-cotraining.github.io/获取。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
