AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains
By Haoshu Fang, Chenxi Wang, Hongjie Fang, Minghao Gou, Jirong Liu, Hengxu Yan, Wenhai Liu, Yichen Xie, Cewu Lu. IEEE Transactions on robotics, 2022
474 citations recorded by the source corpus.
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 article, 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.
Subfields: Biomedical Engineering, Cognitive Neuroscience, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/6e1c0a5f083db4ad691f54878aed36f284bde019
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Citations in the corpus
- ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation (2024, methodology): “In this work, we use AnyGrasp [131] 1 .”
- Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI (2024, unknown): “Models often overfit to simulations, struggle with real-world sensor noise, and fail to handle unexpected events.”
- π0.5: a Vision-Language-Action Model with Open-World Generalization (2025, unknown): “More recently, large-scale datasets collected across many domains [41, 68, 63, 67, 14, 49] have been shown to enable generalization of simple but end-to-end learned tasks to new environments [33, 31, 67, 69, 26, 49, 28, 64].”