EquiDexFlow: Contact-Grounded SE(3)-Equivariant Dexterous Grasp Generative Flows
EquiDexFlow is an SE(3)-equivariant flow-matching model that jointly predicts wrist pose, joint angles, fingertip contacts, surface normals, and contact forces from an object point cloud. By projecting contacts onto the object surface and forces into the Coulomb friction cone by construction, it ensures placement and friction compliance without loss penalties. Experiments show wrist residuals below 0.04° over 200 rotations, zero joint deviation, zero friction violations, and the best composite score among ablations. On a physical robot, retargeted grasps successfully complete open-loop pick-and-hold trials on all six test objects.
[2606.12728] EquiDexFlow: Contact-Grounded SE(3)-Equivariant Dexterous Grasp Generative Flows
[Submitted on 10 Jun 2026]
Title:EquiDexFlow: Contact-Grounded SE(3)-Equivariant Dexterous Grasp Generative Flows
View a PDF of the paper titled EquiDexFlow: Contact-Grounded SE(3)-Equivariant Dexterous Grasp Generative Flows, by Clinton Enwerem and 2 other authors
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Abstract:Most learned dexterous grasp generators relegate contact forces to a downstream verification step, so a kinematically-plausible pose can still violate the conditions for a stable physical grasp. We address this with EquiDexFlow, an SE(3)-equivariant flow-matching model that jointly predicts wrist pose, joint angles, fingertip contacts, surface normals, and contact forces from an object point cloud. Our architecture projects contacts onto the object surface and forces into the Coulomb friction cone by construction, so placement and friction compliance hold without loss penalties. We prove end-to-end SE(3) equivariance and verify it empirically over 200 rotations, with wrist residuals below $0.04^\circ$ and exactly zero joint deviation. Trained on 8,100 force-closure grasps across 81 objects for the 16-DoF Allegro Hand, our model achieves zero friction violations, the best composite score, and the lowest wrench residual among all ablation variants. We retarget decoded fingertip contacts to a 16-DoF LEAP Hand via per-finger inverse kinematics, and our hardware-feasible refinement places every joint at least 5% inside its actuator envelope while preserving wrench balance. On the physical robot, retargeted EquiDexFlow-decoded grasps complete open-loop pick-and-hold trials on all six test objects, with every asymmetric object succeeding at both the canonical pose and a $120^\circ$ co-rotation. Videos, code, and checkpoints are available at this https URL.
Comments: 22 pages, 11 figures, 11 tables. Project page with videos, code, and checkpoints: this https URL
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2606.12728 [cs.RO]
(or arXiv:2606.12728v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.12728
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Clinton Enwerem [view email] [v1] Wed, 10 Jun 2026 22:27:03 UTC (11,410 KB)
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