Learning to Generate 6-DoF Grasp Poses with Reachability Awareness

-- Motivated by the stringent requirements of unstructured real-world where a plethora of unknown objects reside in arbitrary locations of the surface, we propose a voxel-based deep 3D Convolutional Neural Network (3D CNN) that generates feasible 6-DoF grasp poses in unrestricted workspace with reachability awareness. Unlike the majority of works that predict if a proposed grasp pose within the restricted workspace will be successful solely based on grasp pose stability, our approach further learns a reachability predictor that evaluates if the grasp pose is reachable or not from robot's own experience. T o avoid the laborious real training data collection, we exploit the power of simulation to train our networks on a large-scale synthetic dataset. This work is an early attempt that simultaneously evaluates grasping reachability from learned knowledge while proposing feasible grasp poses with 3D CNN. Experimental results in both simulation and real-world demonstrate that our approach outperforms several other methods and achieves 82.5% grasping success rate on unknown objects. I. INTRODUCTION Real-world applications demand robotic manipulation algorithms that are efficient in arbitrary workspace where objects may not be reachable. Figure 1 illustrates a scenario where such an algorithm needs to 1) decide which of the sampled grasp pose candidates are more reachable and 2) grasp as many objects as possible from the dense clutter with minimal efforts. The predominant top-down grasping is often restricted in narrowly prepared workspace [1], whereas practical problems are often in extended and obstacle-rich environments that require flexible 6-DoF grasp poses to reach objects. Albeit extensive researches have been conducted on this topic, the grasping reachability problem remains relatively unexplored.