Picking cluttered general objects is a challenging task due to the complex geometries and various stacking configurations. Many prior works utilize pose estimation for picking, but pose estimation is difficult on cluttered objects. In this paper, we propose Cluttered Objects Descriptors (CODs), a dense cluttered objects descriptor that can represent rich object structures, and use the pre-trained CODs network along with its intermediate outputs to train a picking policy. Additionally, we train the policy with reinforcement learning, which enable the policy to learn picking without supervision. We conduct experiments to demonstrate that our CODs is able to consistently represent seen and unseen cluttered objects, which allowed for the picking policy to robustly pick cluttered general objects. The resulting policy can pick 96.69% of unseen objects in our experimental environment which is twice as cluttered as the training scenarios.

翻译：杂乱通用物体的抓取因复杂的几何形状和多样化的堆叠构型而充满挑战。许多先前工作依赖位姿估计进行抓取，但位姿估计在杂乱物体上难以实现。本文提出杂乱物体描述符（CODs），这是一种能够表征丰富物体结构的密集杂乱物体描述符，并利用预训练的CODs网络及其中间输出训练抓取策略。此外，我们采用强化学习训练该策略，使其无需监督即可学习抓取。实验表明，我们的CODs能够稳定表征已知和未知的杂乱物体，从而使抓取策略能够鲁棒地抓取杂乱通用物体。在实验环境中（其杂乱程度为训练场景的两倍），最终策略对未知物体的抓取成功率达到96.69%。