Transferable Active Grasping and Real Embodied Dataset

• URL: http://arxiv.org/abs/2004.13358v1
• Date: Tue, 28 Apr 2020 08:15:35 GMT
• Title: Transferable Active Grasping and Real Embodied Dataset
• Authors: Xiangyu Chen, Zelin Ye, Jiankai Sun, Yuda Fan, Fang Hu, Chenxi Wang, Cewu Lu
• Abstract summary: We show how to search for feasible viewpoints for grasping by the use of hand-mounted RGB-D cameras. 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.
• Score: 48.887567134129306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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