AGILE：基于元素分解的接近式抓取推断
AGILE: Approach-based Grasp Inference Learned from Element Decomposition
AI总结：
提出AGILE方法，通过元素分解和Mask R-CNN学习基于夹爪接近的抓取推断，在仿真中达到90%已见物体和78%未见物体成功率，经域适应后物理抓取成功率为70%。
摘要：
人类作为抓取检测领域的专家，能够通过考虑手与物体之间的定位信息来抓取物体。本工作提出一种方法，使机器人操作器能够学习同样的能力，根据夹爪接近物体的方式，以最优方式抓取物体。该方法基于深度学习，包含两个主要阶段。为了在网络中泛化到未见过的物体，所提出的基于接近的抓取推断（Approach-based Grasping Inference）涉及一个元素分解阶段，将物体拆分为其主要组成部分，每个部分针对夹爪的特定接近方式标注一个或多个抓取。随后，一个抓取检测网络利用Mask R-CNN分解得到的元素以及夹爪接近的信息，来检测夹爪所接近的元素以及最优抓取。为了训练这些网络，本研究引入了一个在Coppeliasim仿真环境中收集的机器人抓取数据集。该数据集包含10个不同的物体，并带有标注的元素分解掩码和抓取矩形。所提出的方法在Coppeliasim仿真环境中，对已见物体的抓取成功率达到90%，对未见物体的成功率为78%。最后，通过对仿真中收集的训练集施加变换并扩充数据集，进行了仿真到现实的域适应，使用Delta并联机器人和2指夹爪实现了70%的物理抓取成功率。
英文摘要：
Humans, this species expert in grasp detection, can grasp objects by taking into account hand-object positioning information. This work proposes a method to enable a robot manipulator to learn the same, grasping objects in the most optimal way according to how the gripper has approached the object. Built on deep learning, the proposed method consists of two main stages. In order to generalize the network on unseen objects, the proposed Approach-based Grasping Inference involves an element decomposition stage to split an object into its main parts, each with one or more annotated grasps for a particular approach of the gripper. Subsequently, a grasp detection network utilizes the decomposed elements by Mask R-CNN and the information on the approach of the gripper in order to detect the element the gripper has approached and the most optimal grasp. In order to train the networks, the study introduces a robotic grasping dataset collected in the Coppeliasim simulation environment. The dataset involves 10 different objects with annotated element decomposition masks and grasp rectangles. The proposed method acquires a 90% grasp success rate on seen objects and 78% on unseen objects in the Coppeliasim simulation environment. Lastly, simulation-to-reality domain adaptation is performed by applying transformations on the training set collected in simulation and augmenting the dataset, which results in a 70% physical grasp success performance using a Delta parallel robot and a 2 -fingered gripper.
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。
