This paper presents GenH2R, a framework for learning generalizable
vision-based human-to-robot (H2R) handover skills. The goal is to equip robots
with the ability to reliably receive objects with unseen geometry handed over
by humans in various complex trajectories. We acquire such generalizability by
learning H2R handover at scale with a comprehensive solution including
procedural simulation assets creation, automated demonstration generation, and
effective imitation learning. We leverage large-scale 3D model repositories,
dexterous grasp generation methods, and curve-based 3D animation to create an
H2R handover simulation environment named \simabbns, surpassing the number of
scenes in existing simulators by three orders of magnitude. We further
introduce a distillation-friendly demonstration generation method that
automatically generates a million high-quality demonstrations suitable for
learning. Finally, we present a 4D imitation learning method augmented by a
future forecasting objective to distill demonstrations into a visuo-motor
handover policy. Experimental evaluations in both simulators and the real world
demonstrate significant improvements (at least +10\% success rate) over
baselines in all cases. The project page is this https URL

本研究提出了 GenH2R 框架，用于学习通用的基于视觉的人到机器人交接技能，通过大规模的模拟环境和模仿学习的方法，实现了超过现有模拟器数量三个数量级的 H2R 交接模拟环境和高质量演示，通过 4D 模仿学习方法在模拟器和实际中取得了至少 10％的成功率改进。