基于软多模态夹爪与深度多阶段学习方案的混合机器人抓取
Hybrid Robotic Grasping with a Soft Multimodal Gripper and a Deep Multistage Learning Scheme
AI总结：
本文提出一种结合软多模态夹爪的深度强化学习框架，实现多阶段混合机器人抓取，通过三种抓取模式优化动作数量，在仿真和真实实验中最大提升抓取效率161%和154%。
摘要：
抓取一直被认为是机器人操作中一项重要且实用的任务。然而，实现对不同物体的鲁棒且高效的抓取极具挑战性，因为它涉及夹爪设计、感知、控制和学习等方面。近期基于学习的方法在抓取各种新颖物体方面表现出色。然而，这些方法要么通常局限于单一抓取模式，要么需要更多的末端执行器来抓取不同物体。此外，夹爪设计和学习方法通常是分开开发的，这可能无法充分挖掘多模态夹爪的能力。在本文中，我们提出了一种深度强化学习（DRL）框架，利用新型软多模态夹爪实现多阶段混合机器人抓取。具有三种抓取模式（即包络、吸附以及先包络后吸附）的软夹爪既能处理不同形状的物体，也能同时抓取多个物体。我们提出了一种与多模态夹爪相结合的新型混合抓取方法，以优化抓取动作的数量。我们在不同场景下（即具有两种抓取类型物体的不同比例）评估了DRL框架。结果表明，与单一抓取模式相比，所提算法减少了抓取动作数量（即提升了抓取效率，在仿真中最大提升161%，在真实世界实验中最大提升154%）。
英文摘要：
Grasping has long been considered an important and practical task in robotic manipulation. Yet achieving robust and efficient grasps of diverse objects is challenging, since it involves gripper design, perception, control and learning, etc. Recent learning-based approaches have shown excellent performance in grasping a variety of novel objects. However, these methods either are typically limited to one single grasping mode, or else more end effectors are needed to grasp various objects. In addition, gripper design and learning methods are commonly developed separately, which may not adequately explore the ability of a multimodal gripper. In this paper, we present a deep reinforcement learning (DRL) framework to achieve multistage hybrid robotic grasping with a new soft multimodal gripper. A soft gripper with three grasping modes (i.e., enveloping, sucking, and enveloping_then_sucking) can both deal with objects of different shapes and grasp more than one object simultaneously. We propose a novel hybrid grasping method integrated with the multimodal gripper to optimize the number of grasping actions. We evaluate the DRL framework under different scenarios (i.e., with different ratios of objects of two grasp types). The proposed algorithm is shown to reduce the number of grasping actions (i.e., enlarge the grasping efficiency, with maximum values of 161% in simulations and 154% in real-world experiments) compared to single grasping modes.
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。
