MotionTrans：人类VR数据赋能机器人操作策略的运动级学习
MotionTrans: Human VR Data Enable Motion-Level Learning for Robotic Manipulation Policies
AI总结：
本文提出MotionTrans框架，通过多任务人机协同训练将人类VR数据中的运动直接迁移到机器人策略，实现13个任务的运动级迁移（9个零样本成功），并揭示了协同训练与运动覆盖的关键作用。
摘要：
扩展真实机器人数据是模仿学习中的关键瓶颈，这促使人们使用辅助数据进行策略训练。虽然机器人操作的其他方面（如图像或语言理解）可以从基于互联网的数据集中学习，但获取运动知识仍然具有挑战性。人类数据以其丰富多样的操作行为，为此提供了宝贵的资源。尽管先前的工作表明使用人类数据可以带来好处，例如提高鲁棒性和训练效率，但尚不清楚它能否实现其最大优势：使机器人策略能够直接学习新运动以完成任务。在本文中，我们通过多任务人机协同训练系统地探索了这一潜力。我们引入了MotionTrans，这是一个包含数据收集系统、人类数据转换流程和加权协同训练策略的框架。通过同时协同训练30个人机任务，我们直接将13个任务的运动从人类数据迁移到可部署的端到端机器人策略中。值得注意的是，9个任务以零样本方式取得了非平凡的成功率。MotionTrans还显著提升了预训练-微调性能（成功率提升40%）。通过消融研究，我们还确定了成功运动学习的关键因素：与机器人数据协同训练以及广泛的任务相关运动覆盖。这些发现释放了从人类数据中进行运动级学习的潜力，为其有效用于训练机器人操作策略提供了见解。所有数据、代码和模型权重均已开源：https://motiontrans.github.io/。
英文摘要：
Scaling real robot data is a key bottleneck in imitation learning, leading to the use of auxiliary data for policy training. While other aspects of robotic manipulation such as image or language understanding may be learned from internet-based datasets, acquiring motion knowledge remains challenging. Human data, with its rich diversity of manipulation behaviors, offers a valuable resource for this purpose. While previous works show that using human data can bring benefits, such as improving robustness and training efficiency, it remains unclear whether it can realize its greatest advantage: enabling robot policies to directly learn new motions for task completion. In this paper, we systematically explore this potential through multi-task human-robot cotraining. We introduce MotionTrans, a framework that includes a data collection system, a human data transformation pipeline, and a weighted cotraining strategy. By cotraining 30 human-robot tasks simultaneously, we direcly transfer motions of 13 tasks from human data to deployable end-to-end robot policies. Notably, 9 tasks achieve non-trivial success rates in zero-shot manner. MotionTrans also significantly enhances pretraining-finetuning performance (+40% success rate). Through ablation study, we also identify key factors for successful motion learning: cotraining with robot data and broad task-related motion coverage. These findings unlock the potential of motion-level learning from human data, offering insights into its effective use for training robotic manipulation policies. All data, code, and model weights are open-sourced https://motiontrans.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。
