Large real-world robot datasets hold great potential to train generalist robot models, but scaling real-world human data collection is time-consuming and resource-intensive. Simulation has great potential in supplementing large-scale data, especially with recent advances in generative AI and automated data generation tools that enable scalable creation of robot behavior datasets. However, training a policy solely in simulation and transferring it to the real world often demands substantial human effort to bridge the reality gap. A compelling alternative is to co-train the policy on a mixture of simulation and real-world datasets. Preliminary studies have recently shown this strategy to substantially improve the performance of a policy over one trained on a limited amount of real-world data. Nonetheless, the community lacks a systematic understanding of sim-and-real co-training and what it takes to reap the benefits of simulation data for real-robot learning. This work presents a simple yet effective recipe for utilizing simulation data to solve vision-based robotic manipulation tasks. We derive this recipe from comprehensive experiments that validate the co-training strategy on various simulation and real-world datasets. Using two domains--a robot arm and a humanoid--across diverse tasks, we demonstrate that simulation data can enhance real-world task performance by an average of 38%, even with notable differences between the simulation and real-world data. Videos and additional results can be found at https://co-training.github.io/

翻译：大规模真实世界机器人数据集在训练通用机器人模型方面具有巨大潜力，但扩展真实世界人类数据收集工作耗时且资源密集。仿真技术在大规模数据补充方面展现出显著优势，特别是随着生成式人工智能和自动化数据生成工具的近期进展，使得可扩展的机器人行为数据集创建成为可能。然而，仅在仿真环境中训练策略并将其迁移至现实世界，通常需要大量人力来弥合现实差距。一种更具吸引力的替代方案是在仿真与真实世界混合数据集上对策略进行协同训练。初步研究近期表明，相较于仅使用有限真实数据训练的策略，该策略能显著提升策略性能。尽管如此，学术界仍缺乏对仿真-真实协同训练的系统性理解，以及如何有效利用仿真数据促进真实机器人学习。本研究提出一种简单而有效的方案，利用仿真数据解决基于视觉的机器人操作任务。该方案源自我们在多种仿真与真实数据集上验证协同训练策略的综合性实验。通过在机器人手臂和人形机器人两个领域进行多样化任务测试，我们证明即使仿真与真实数据存在显著差异，仿真数据仍能将真实世界任务性能平均提升38%。视频及补充结果详见 https://co-training.github.io/