CoorGrasp: Coordinated Contact Control for Adaptive Dexterous Grasping Under Uncertainty
For robotic manipulation, this work addresses the open-loop execution gap in dexterous grasping, improving robustness under uncertainty.
This paper proposes a tactile-driven model predictive controller for adaptive execution of dexterous grasps under shape and position uncertainty, achieving higher grasp success rates and reduced undesired object movements in simulations (15k grasps across 478 objects) and real-world experiments (8 objects).
While recent research has focused heavily on dexterous grasp pose generation, less attention has been devoted to the execution of planned grasps. Under shape and position uncertainty, open-loop execution often yields uncoordinated contacts, causing undesired in-hand object motion and even grasp failures. To address this, this paper proposes a tactile-driven model predictive controller for adaptive and delicate execution of diverse dexterous grasps. Our approach emphasizes multi-contact coordination across both approaching and grasping phases, with three key novelties: (i) coordination-aware phase separation, (ii) arm-hand coordination to compensate for position errors, and (iii) adaptive force coordination to increase contact forces in a balanced manner. An analytical model is employed to relate contact forces to robot joint motions for predictive control. Our formulation imposes no restrictions on grasp types or contact configurations and integrates seamlessly with state-of-the-art grasp pose generation methods. We validate the approach through large-scale simulations involving 15k grasps across 478 objects on three robotic hands, and real-world experiments on 8 objects. Results demonstrate that our method achieves higher grasp success rates and reduced undesired object movements.