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AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency
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We introduce an efficient approach for learning dexterous grasping with minimal data, advancing robotic manipulation capabilities across different robotic hands. Unlike traditional methods that require millions of grasp labels for each robotic hand, our method achieves high performance with human-level learning efficiency: only hundreds of grasp attempts on 40 training objects. The approach separates the grasping process into two stages: first, a universal model maps scene geometry to intermediate contact-centric grasp representations, independent of specific robotic hands. Next, a unique grasp decision model is trained for each robotic hand through real-world trial and error, translating these representations into final grasp poses. Our results show a grasp success rate of 75-95\% across three different robotic hands in real-world cluttered environments with over 150 novel objects, improving to 80-98\% with increased training objects. This adaptable method demonstrates promising applications for humanoid robots, prosthetics, and other domains requiring robust, versatile robotic manipulation.
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Cited by 9 Pith papers
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DexMani: Human-Derived Manipulability Guidance for Dexterous Rotation
A single human-derived prior over how rotational manipulability evolves improves dexterous rotation success across robot hands, tasks, and unseen objects.
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PhotoHOI: Synthesizing 3D Hand-Object Interactions from a Single RGB Photograph
PhotoHOI turns one RGB photo plus an open-vocabulary instruction into a scene-grounded 3D hand-object motion sequence by parsing the task, recovering objects, planning object motion, and optimizing grasps in a learned...
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GraspGraphNet: Graph-Structured Multi-Embodiment Dexterous Grasp Generation
A single URDF-graph flow-matching model generates executable grasps for Barrett, Allegro, and Shadow hands at 83.48% average success and 40 ms, and reaches 72.70% on finger-removal variants without retraining.
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HERO: Learning Humanoid End-Effector Control for Visual Whole-Body Open-Vocabulary Object Grasping
HERO achieves 2.44 cm end-effector tracking error on a Unitree G1 humanoid and uses it, with open-vocabulary perception, to grasp novel objects at up to 90% success in diverse real scenes.
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Scaling Cross-Embodiment World Models for Dexterous Manipulation
A single particle-based world model trained on many simulated robot hands and real human hands can plan dexterous manipulation on robot hands it never trained on.
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LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations
LodeStar combines automatic skill segmentation with simulation-based reinforcement learning augmentation and a learned routing transformer to let a robotic hand complete long-horizon dexterous tasks from a few human demos.
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TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types
A type-guided teleoperation system that selects predefined dexterous hand poses with a language model outperforms retargeting-based teleoperation on nine real-world tasks and improves imitation learning success.
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AnyDexRT: Calibration-Free Dexterous Hand Retargeting with Few-Shot Human Guidance
Self-supervised fingertip mapping with few-shot human anchors and a pinch contact classifier yields calibration-free, more intuitive retargeting across diverse human-like robot hands.
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AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion
AdaDexGrasp learns to fuse point clouds with finger-level tactile labels to generate, judge, and correct dexterous grasps, reporting 91%/82%/83% success on seen, unseen-object, and unseen-category sets in simulation.
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