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.
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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.