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.
Anygrasp: Robust and efficient grasp perception in spatial and temporal domains.IEEE Transactions on Robotics, 2023
3 Pith papers cite this work. Polarity classification is still indexing.
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AFUN predicts task-conditional functional masks and 3D post-contact motion curves from RGB-D and language, trained via a standardized multi-source data pipeline, and reports large gains over baselines on segmentation, contact prediction, and motion tasks.
Vision-guided dual-arm robotic pipeline achieves 8/10 success disassembling 21-cell 18650 packs from arbitrary poses with 2.4 mm localization error and 6-minute cycle time using RGB-D sensing and general grippers.
citing papers explorer
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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.
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AFUN: Towards an Affordance Foundation Model for Functionality Understanding
AFUN predicts task-conditional functional masks and 3D post-contact motion curves from RGB-D and language, trained via a standardized multi-source data pipeline, and reports large gains over baselines on segmentation, contact prediction, and motion tasks.
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Vision-Guided Dual-Arm Humanoid Robotic Disassembly of End-of-Life 18650 Lithium-ion Battery Packs
Vision-guided dual-arm robotic pipeline achieves 8/10 success disassembling 21-cell 18650 packs from arbitrary poses with 2.4 mm localization error and 6-minute cycle time using RGB-D sensing and general grippers.