GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable
  Simulation, Demonstration, and Imitation
Zifan Wang Junyu Chen Ziqing Chen Pengwei Xie Rui Chen Li Yi
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 https://GenH2R.github.io/.