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RobustDexGrasp: Robust Dexterous Grasping of General Objects
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RobustDexGrasp: Robust Dexterous Grasping of General Objects
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The ability to robustly grasp a variety of objects is essential for dexterous robots. In this paper, we present a framework for zero-shot dynamic dexterous grasping using single-view visual inputs, designed to be resilient to various disturbances. Our approach utilizes a hand-centric object shape representation based on dynamic distance vectors between finger joints and object surfaces. This representation captures the local shape around potential contact regions rather than focusing on detailed global object geometry, thereby enhancing generalization to shape variations and uncertainties. To address perception limitations, we integrate a privileged teacher policy with a mixed curriculum learning approach, allowing the student policy to effectively distill grasping capabilities and explore for adaptation to disturbances. Trained in simulation, our method achieves success rates of 97.0% across 247,786 simulated objects and 94.6% across 512 real objects, demonstrating remarkable generalization. Quantitative and qualitative results validate the robustness of our policy against various disturbances.
Forward citations
Cited by 12 Pith papers
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HUGS: Guiding Unified Dexterous Grasp Synthesis Across Modes and Scales via Learned Human Priors
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Dexora: Open-source VLA for High-DoF Bimanual Dexterity
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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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PAKE: Learning Whole-Body Loco-Manipulation with Partial Kinematic Embeddings
A hierarchical controller using a kinematic normalizing flow for partial inverse-kinematics redundancy plus low-level imitation yields 4.5 cm / 0.14 rad end-effector tracking while walking on a real quadruped-arm platform.
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Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation
Zero-shot sim-to-real RL policies on a five-finger hand achieve commandable grasp-force tracking and in-hand reorientation using dense tactile simulation, current-to-torque calibration, and actuator randomization.
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Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control
A multi-agent RL high-level planner outputs task-space velocities that a GPU-parallel QP low-level controller converts to joint velocities while enforcing limits and collisions, yielding robust sim-to-real dexterous g...
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On Data Thinning for Model Validation in Small Area Estimation
Thinned-data MSE for small-area models is unbiased for a risk that systematically differs from full-data risk; under Fay-Herriot the gap is closed-form in the model's shrinkage, and the thinning fraction faces a sharp...
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Learning Dexterous Grasping from Sparse Taxonomy Guidance
Dense spatial modulation of multiple 3D LUTs plus an uncertainty-weighted reconstruction loss improves mixed under/over-exposure correction over global-modulation baselines on four benchmarks.
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Learning Dexterous Grasping from Sparse Taxonomy Guidance
GRIT learns dexterous grasping from sparse taxonomy guidance, achieving 87.9% success and better generalization to novel objects via a two-stage prediction-plus-policy approach.
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Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration
Dexplore learns dexterous robotic hand control from human MoCap demonstrations by treating them as soft, adaptively shrinking spatial references, then distills the policy into a vision-based controller.
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FastGrasp: Learning-based Whole-body Control method for Fast Dexterous Grasping with Mobile Manipulators
FastGrasp uses two-stage RL with CVAE for diverse grasp candidates from point clouds and tactile sensing for impact adjustments to achieve robust fast whole-body grasping in sim and real-world settings.
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