We study the problem of robotic stacking with objects of complex geometry. We
propose a challenging and diverse set of such objects that was carefully
designed to require strategies beyond a simple "pick-and-place" solution. Our
method is a reinforcement learning (RL) approach combined with vision-based
interactive policy distillation and simulation-to-reality transfer. Our learned
policies can efficiently handle multiple object combinations in the real world
and exhibit a large variety of stacking skills. In a large experimental study,
we investigate what choices matter for learning such general vision-based
agents in simulation, and what affects optimal transfer to the real robot. We
then leverage data collected by such policies and improve upon them with
offline RL. A video and a blog post of our work are provided as supplementary
material.

本文使用增强学习方法和视觉技术在真实环境中完成对复杂物体进行叠放的任务，重点研究了这种方法的优越性和训练问题。