Real-Time Generative Grasping with Spatio-temporal Sparse Convolution

Robots performing mobile manipulation in unstructured environments must identify grasp affordances quickly and with robustness to perception noise. Yet in domains such as underwater manipulation, where perception noise is severe, computation is constrained, and the environment is dynamic, existing techniques fail. They are too computationally demanding, or too sensitive to noise to allow for closed loop grasping or dynamic replanning, or do not consider 6-DOF grasps. We present a novel grasp synthesis network, TSGrasp, that uses spatio-temporal sparse convolution to process a streaming point cloud in real time. The network generates 6-DOF grasps at greater speed and with less memory than Contact GraspNet, a state-of-the-art algorithm based on Point-Net++. By considering information from multiple successive frames of depth video, TSGrasp boosts robustness to noise or temporary self-occlusion and allows more grasps to be rapidly identified. Our grasp synthesis system was successfully demonstrated in an underwater environment with a Blueprint Labs Bravo robotic arm.