  Grasping in cluttered scenes is challenging for robot vision systems, as
detection accuracy can be hindered by partial occlusion of objects. We adopt a
reinforcement learning (RL) framework and 3D vision architectures to search for
feasible viewpoints for grasping by the use of hand-mounted RGB-D cameras. To
overcome the disadvantages of photo-realistic environment simulation, we
propose a large-scale dataset called Real Embodied Dataset (RED), which
includes full-viewpoint real samples on the upper hemisphere with amodal
annotation and enables a simulator that has real visual feedback. Based on this
dataset, a practical 3-stage transferable active grasping pipeline is
developed, that is adaptive to unseen clutter scenes. In our pipeline, we
propose a novel mask-guided reward to overcome the sparse reward issue in
grasping and ensure category-irrelevant behavior. The grasping pipeline and its
possible variants are evaluated with extensive experiments both in simulation
and on a real-world UR-5 robotic arm.
