Improving Grasp Pose Detection by Implicitly Utilizing Geometric Information and Spatial Relations of Objects in Clutter

6 degrees of freedom (6-DoF) grasp pose detection in clutter is an essential but challenging task in the vision measurement. However, most current detection methods often fail to consider the geometric information and partial occlusions of objects in clutter, resulting in inaccurate robotic grasping and potential collisions. In this work, we propose an end-to-end grasping system to directly generate 6-DoF grasp poses from point clouds in clutter. Our system first selects the most feasible positions as the grasp centers, and then determines their grasp parameters, such as approaching vectors and in-plane rotations. Specifically, we design an optimality criterion based on the geometric information of objects to extract rich object features from the scene and identify high-graspablity areas. To alleviate the collision problem caused by occlusions, we focus on the spatial relations among objects and explicitly train a collision predictor to directly select collision-free points as grasp centers from high-graspablity areas. Based on these grasp centers, our model can easily generate diverse sets of collision-free and robust grasp poses for successful grasping. To efficiently determine grasp parameters, we exploit neighbor search to capture the local context around each grasp center and then implicitly learn relations among objects using the self-attention mechanism. Compared with previous methods, our grasping system improves performance by a large margin (>10%), and achieves state-of-the-art results on GraspNet-1Billion, a large-scale dataset. Besides, our method has a high success rate for grasping in simulation and real-world experiments, verifying the validity in clutter.