MoDex: A Diffusion Policy for Sequential Multi-Object Dexterous Grasping
MoDex is a diffusion-based policy that enables a dexterous hand to sequentially grasp multiple objects without releasing those already held. By conditioning on opposition space and point cloud, it uses only a subset of finger degrees of freedom per grasp. Two-stage training (imitation learning + RL fine-tuning) improves success in simulation and real world.
[2606.05407] MoDex: A Diffusion Policy for Sequential Multi-Object Dexterous Grasping
[Submitted on 3 Jun 2026]
Title:MoDex: A Diffusion Policy for Sequential Multi-Object Dexterous Grasping
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Abstract:This work addresses sequentially grasping multiple objects with a single dexterous hand without releasing those already held. Most dexterous grasping methods commit all of the hand's degrees of freedom to a single object, underutilizing its dexterity and leaving no redundancy for subsequent grasps. The proposed solution, MoDex, is a diffusion policy that predicts the next gripper pose directly from observations, conditioned on an opposition space and point cloud. The opposition space condition specifies which fingers participate in the current grasp, enabling the gripper to use only a subset of its available degrees of freedom while reserving the remaining degrees of freedom for subsequent grasps. To facilitate sim-to-real transfer, MoDex is trained in two stages: first through imitation learning on expert demonstrations, and subsequently through reinforcement learning fine-tuning, which consistently improves success rates over the pre-trained policy. We evaluate MoDex in simulation on a MuJoCo-based Franka Emika Panda robot equipped with an Allegro Hand and on the corresponding real-world hardware platform. Across both simulation and real-world experiments, MoDex achieves higher success rates than the evaluated learning-based baselines, improving performance by 2.92-17.92% and 6.67-17.78%, respectively. Project page: this https URL.
Comments: Submitted to CoRL 2026
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.05407 [cs.RO]
(or arXiv:2606.05407v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.05407
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Haofei Lu [view email] [v1] Wed, 3 Jun 2026 20:22:10 UTC (7,739 KB)
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