aiqbench analysis
This paper addresses a critical gap in robotic manipulation by shifting focus from grasp planning to the execution phase under uncertainty, utilizing tactile-driven model predictive control to coordinate multi-contact forces and arm-hand movements. By integrating analytical models that relate contact forces directly to joint motions, the approach significantly improves success rates for delicate tasks across diverse object shapes without restricting specific grasp configurations. The method's validation through extensive simulations on three robotic hands demonstrates its potential to enhance dexterity in real-world applications where open-loop execution often fails due to positioning errors.
CoorGrasp: Coordinated Contact Control for Adaptive Dexterous Grasping Under Uncertainty
arXiv:2607.03557v2 Announce Type: replace Abstract: 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.
Abstract: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.
Accepted by ICRA 2026. Best Poster Award at ICRA 2026 Workshop on Dexterity with Multifingered Hands: Hardware, Sensing, and Skills. Project Website: this https URL
Robotics (cs.RO)
arXiv:2607.03557 [cs.RO]
(or arXiv:2607.03557v2 [cs.RO] for this version)
Full text under the publisher's open licence · 211 words
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