arXiv:2606.12728v2 Announce Type: replace-cross 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 frict
The continuous advancements in AI and robotics, coupled with increasing computational power, are enabling more sophisticated and integrated models for complex tasks like dexterous manipulation.
This development significantly enhances the capabilities of robotic systems to perform delicate and precise manipulation, moving beyond simple grasping to stable, force-aware interactions essential for real-world applications.
Grasp generation models now inherently integrate contact forces and surface normals, ensuring physical stability rather than just kinematic plausibility, leading to more reliable and functional robotic systems.
- · Robotics companies
- · Automation industries
- · AI research institutions
- · Logistics and manufacturing
- · Companies reliant on primitive robotic manipulation
- · Manual labor in highly precise assembly
Robots will achieve higher success rates in complex manipulation tasks in unstructured environments.
This will accelerate the deployment of autonomous robots in logistics, healthcare, and advanced manufacturing.
The increased dexterity could lead to breakthroughs in areas requiring fine motor skills, potentially enabling humanoid robots to perform a wider range of human-centric tasks.
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Read at arXiv cs.LG