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.

本文提出了一种密集的杂乱物品描述符，即 Cluttered Objects Descriptors (CODs)，并结合其中间输出和预训练的 CODs 网络训练选取策略，使用强化学习方法使其能够在累积的情况下学习选取杂乱的一般物品，实验结果显示出具有稳健性的选取策略可以选取 96.69% 的未见过的物品。

使用稠密物体描述符进行杂乱物品拾取的强化学习

Reinforcement Learning for Picking Cluttered General Objects with Dense  Object Descriptors

Object detection has been a building block in computer vision. Though
considerable progress has been made, there still exist challenges for objects
with small size, arbitrary direction, and dense distribution. Apart from
natural images, such issues are especially pronounced for aerial images of
great importance. This paper presents a novel multi-category rotation detector
for small, cluttered and rotated objects, namely SCRDet. Specifically, a
sampling fusion network is devised which fuses multi-layer feature with
effective anchor sampling, to improve the sensitivity to small objects.
Meanwhile, the supervised pixel attention network and the channel attention
network are jointly explored for small and cluttered object detection by
suppressing the noise and highlighting the objects feature. For more accurate
rotation estimation, the IoU constant factor is added to the smooth L1 loss to
address the boundary problem for the rotating bounding box. Extensive
experiments on two remote sensing public datasets DOTA, NWPU VHR-10 as well as
natural image datasets COCO, VOC2007 and scene text data ICDAR2015 show the
state-of-the-art performance of our detector. The code and models will be
available at this https URL

本文提出一种新颖的旋转多分类目标检测器 SCRDet，旨在解决小目标、杂乱目标和旋转目标检测的问题。并通过使用采样融合网络，以及监督像素注意力网络和通道注意力网络，来提高对小目标的灵敏度和准确度。实验结果显示该检测器在各类公共数据集上都表现出了最先进的性能。