用合成数据训练机器人在移动中完成人机交接，效果优于真实数据。
MobileH2R: Learning Generalizable Human to Mobile Robot Handover Exclusively from Scalable and Diverse Synthetic Data
- 基于高保真合成数据生成多样人体动作，无需真实示范。
- 在仿真与真实环境中均实现至少15%成功率提升。
- 适合需要通用交接能力的移动机器人研发人员。
本文提出MobileH2R框架，用于学习可泛化的视觉驱动人机移动机器人交接技能。与传统固定基座交接不同，该任务要求移动机器人在大工作空间中可靠接收物体。核心洞察是：通过高质量合成数据在模拟器中即可发展通用交接技能，无需真实世界演示。为此，我们设计了可扩展的全身体态动作数据生成管道，提出自动化创建安全且易模仿示范的方法，并开发高效的4D模仿学习算法，将大规模示范转化为具备基座-机械臂协同能力的闭环策略。仿真与真实环境中的实验表明，在所有情况下相比基线方法成功率至少提升15%。实验还验证了大规模、多样化合成数据对机器人学习的显著促进作用，凸显本框架的可扩展性。
原文摘要 · Abstract (English)
This paper introduces MobileH2R, a framework for learning generalizable vision-based human-to-mobile-robot (H2MR) handover skills. Unlike traditional fixed-base handovers, this task requires a mobile robot to reliably receive objects in a large workspace enabled by its mobility. Our key insight is that generalizable handover skills can be developed in simulators using high-quality synthetic data, without the need for real-world demonstrations. To achieve this, we propose a scalable pipeline for generating diverse synthetic full-body human motion data, an automated method for creating safe and imitation-friendly demonstrations, and an efficient 4D imitation learning method for distilling large-scale demonstrations into closed-loop policies with base-arm coordination. Experimental evaluations in both simulators and the real world show significant improvements (at least +15% success rate) over baseline methods in all cases. Experiments also validate that large-scale and diverse synthetic data greatly enhances robot learning, highlighting our scalable framework.
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