Grasp pretraining on 355k trajectories improves full-task success on six articulated tool-use tasks by 33.3 pp over DP3 in real-world experiments.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MoDex is a diffusion policy conditioned on opposition space and point cloud, trained first by imitation learning then RL fine-tuning, that reports higher success rates than baselines for sequential multi-object dexterous grasping in simulation and real-world tests.
citing papers explorer
-
From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation
Grasp pretraining on 355k trajectories improves full-task success on six articulated tool-use tasks by 33.3 pp over DP3 in real-world experiments.
-
MoDex: A Diffusion Policy for Sequential Multi-Object Dexterous Grasping
MoDex is a diffusion policy conditioned on opposition space and point cloud, trained first by imitation learning then RL fine-tuning, that reports higher success rates than baselines for sequential multi-object dexterous grasping in simulation and real-world tests.