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Nowadays robots play an increasingly important role in our daily life. Inhuman-centered environments, robots often encounter piles of objects, packeditems, or isolated objects. Therefore, a robot must be able to grasp andmanipulate different objects in various situations to help humans with dailytasks. In this paper, we propose a multi-view deep learning approach to handlerobust object grasping in human-centric domains. In particular, our approachtakes a point cloud of an arbitrary object as an input, and then, generatesorthographic views of the given object. The obtained views are finally used toestimate pixel-wise grasp synthesis for each object. We train the modelend-to-end using a small object grasp dataset and test it on both simulationsand real-world data without any further fine-tuning. To evaluate theperformance of the proposed approach, we performed extensive sets ofexperiments in three scenarios, including isolated objects, packed items, andpile of objects. Experimental results show that our approach performed verywell in all simulation and real-robot scenarios, and is able to achievereliable closed-loop grasping of novel objects across various sceneconfigurations.

MVGrasp: Real-Time Multi-View 3D Object Grasping in Highly Cluttered Environments