As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as humans. Our innate grasping system is prompt, accurate, flexible, and continuous across spatial and temporal domains. Few existing methods cover all these properties for robot grasping. In this paper, we propose AnyGrasp for grasp perception to enable robots these abilities using a parallel gripper. Specifically, we develop a dense supervision strategy with real perception and analytic labels in the spatial-temporal domain. Additional awareness of objects' center-of-mass is incorporated into the learning process to help improve grasping stability. Utilization of grasp correspondence across observations enables dynamic grasp tracking. Our model can efficiently generate accurate, 7-DoF, dense, and temporally-smooth grasp poses and works robustly against large depth-sensing noise. Using AnyGrasp, we achieve a 93.3% success rate when clearing bins with over 300 unseen objects, which is on par with human subjects under controlled conditions. Over 900 mean-picks-per-hour is reported on a single-arm system. For dynamic grasping, we demonstrate catching swimming robot fish in the water. Our project page is at https://graspnet.net/anygrasp.html

翻译：作为抓取操作的基础，使机器人具备如人类般稳健的抓取能力至关重要。人类先天抓取系统具备即时、精准、灵活且跨时空连续的特性。现有机器人抓取方法鲜少涵盖全部这些特性。本文提出面向抓取感知的AnyGrasp方法，使机器人能借助平行夹爪实现上述能力。具体而言，我们基于时空域的真实感知与分析标签，开发了密集监督策略。学习过程中融入对物体质心的额外感知，以提升抓取稳定性。利用观测间抓取对应关系实现动态抓取跟踪。我们的模型能够高效生成精确、7自由度、密集且时间平滑的抓取姿态，并对大量深度传感噪声保持鲁棒性。基于AnyGrasp，我们在清理包含300余个未见物体的料箱时达到93.3%的成功率，与受控条件下的人类受试者表现相当。单臂系统的平均抓取效率超过每小时900次。在动态抓取方面，我们演示了在水下抓取游泳机器鱼。项目页面详见https://graspnet.net/anygrasp.html。