超越Pick-and-Place：解决机器人堆叠的多样性
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
摘要：
我们研究的问题，机器人堆叠与复杂的几何形状的对象。我们提出了一个具有挑战性的和多样化的一套这样的对象，精心设计，需要超越一个简单的“取放”的解决方案的战略。我们的方法是一个强化学习（RL）的方法结合了基于视觉的交互式政策蒸馏和模拟到现实的转移。我们学习的策略可以有效地处理现实世界中的多个对象组合，并表现出各种各样的堆叠技能。在一项大型实验研究中，我们研究了在模拟中学习这种基于视觉的通用代理的选择，以及影响最佳传输到真实机器人的因素。然后，我们利用这些策略收集的数据，并通过离线RL对其进行改进。我们的工作的视频和博客文章作为补充材料提供。
英文摘要：
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.
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。
