  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.
