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AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency
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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.
Forward citations
Cited by 12 Pith papers
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Human Universal Grasping
HUG trains a flow-matching model on a new 1M-frame egocentric human grasp dataset to generate retargetable grasps from single RGB-D images, beating baselines by 23-34% on a new 90-object benchmark.
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Mobile UMI: Cross-View Diffusion Policy with Decoupled Kinematics for Mobile Manipulation
A hardware-free dual-camera capture framework with ChArUco spatial unification and receding-horizon state alignment enables decoupled SE(3) manipulation and SE(2) base trajectories for diffusion policies, yielding 83....
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BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes
BiDexGrasp supplies a 9.7-million-grasp bimanual dexterous dataset built via two-stage synthesis and a coordinated geometry-size-adaptive model that generates grasps for unseen objects.
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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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LACE: Latent Visual Representation for Cross-Embodiment Learning
LACE aligns human-robot visual features via semantic distribution matching on corresponding body parts plus Gram loss, yielding 65% better zero-shot policy transfer than baseline DINO.
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Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding
Contact-Grounded Policy predicts coupled robot-state and tactile trajectories with a diffusion model and maps them via a learned consistency function to executable targets for compliance controllers, outperforming sta...
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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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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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Towards a Multi-Embodied Grasping Agent
A JAX-implemented flow-based equivariant model for multi-embodiment grasping that deduces kinematics from geometry to support variable-DoF grippers with a new dataset of 25k scenes and 20M grasps.
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DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy
DexTeleop-0 adds a tactile-driven adaptation loop to bimanual dexterous teleoperation that estimates contact points and applies localized force-compliant corrections via operational-space Jacobian updates.
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GraspSense: Physically Grounded Grasp and Grip Planning for a Dexterous Robotic Hand via Language-Guided Perception and Force Maps
GraspSense computes force maps from object geometry to select mechanically safe grasp regions and regulate grip forces for dexterous hands.
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