Hybrid Robotic Grasping With a Soft Multimodal Gripper and a Deep Multistage Learning Scheme

抓住 方案(数学) 人工智能 计算机科学 对象(语法) 夹持器 强化学习 任务(项目管理) 控制工程 计算机视觉 工程类 机械工程 数学 数学分析 系统工程 程序设计语言
作者
Fukang Liu,Fuchun Sun,Bin Fang,Xiang Li,Songyu Sun,Huaping Liu
出处
期刊:IEEE Transactions on Robotics [Institute of Electrical and Electronics Engineers]
卷期号:39 (3): 2379-2399 被引量:68
标识
DOI:10.1109/tro.2023.3238910
摘要

Grasping has long been considered an important and practical task in robotic\nmanipulation. Yet achieving robust and efficient grasps of diverse objects is\nchallenging, since it involves gripper design, perception, control and\nlearning, etc. Recent learning-based approaches have shown excellent\nperformance in grasping a variety of novel objects. However, these methods\neither are typically limited to one single grasping mode, or else more end\neffectors are needed to grasp various objects. In addition, gripper design and\nlearning methods are commonly developed separately, which may not adequately\nexplore the ability of a multimodal gripper. In this paper, we present a deep\nreinforcement learning (DRL) framework to achieve multistage hybrid robotic\ngrasping with a new soft multimodal gripper. A soft gripper with three grasping\nmodes (i.e., enveloping, sucking, and enveloping_then_sucking) can both deal\nwith objects of different shapes and grasp more than one object simultaneously.\nWe propose a novel hybrid grasping method integrated with the multimodal\ngripper to optimize the number of grasping actions. We evaluate the DRL\nframework under different scenarios (i.e., with different ratios of objects of\ntwo grasp types). The proposed algorithm is shown to reduce the number of\ngrasping actions (i.e., enlarge the grasping efficiency, with maximum values of\n161% in simulations and 154% in real-world experiments) compared to single\ngrasping modes.\n
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