FingerEye delivers continuous vision-tactile sensing via binocular RGB cameras and marker-tracked compliant ring deformation, supporting imitation learning policies that generalize across object variations for tasks like coin standing and syringe manipulation.
Leap hand: Low-cost, efficient, and anthropomor- phic hand for robot learning
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
Proposes Rodrigues Network using a learnable Neural Rodrigues Operator to add kinematic inductive biases for improved robot action learning and prediction.
A unified parametric framework optimizes dexterous hand designs by combining structure, kinematics, and fine surface geometry for grasp stability in simulation and real-world tests.
TRR combines multi-band Riemannian features with a GRU to decode high-dimensional finger kinematics from EMG, achieving 9.79° intra-subject and 16.71° cross-subject average absolute error while running at ~10 Hz on a Raspberry Pi.
A unified parameter space and canonical URDF enable cross-embodiment dexterous grasping policies with 81.9% zero-shot success on unseen hands like the 3-finger LEAP Hand.
citing papers explorer
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FingerEye: Continuous and Unified Vision-Tactile Sensing for Dexterous Manipulation
FingerEye delivers continuous vision-tactile sensing via binocular RGB cameras and marker-tracked compliant ring deformation, supporting imitation learning policies that generalize across object variations for tasks like coin standing and syringe manipulation.
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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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Rodrigues Network for Learning Robot Actions
Proposes Rodrigues Network using a learnable Neural Rodrigues Operator to add kinematic inductive biases for improved robot action learning and prediction.
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Function-based Parametric Co-Design Optimization of Dexterous Hands
A unified parametric framework optimizes dexterous hand designs by combining structure, kinematics, and fine surface geometry for grasp stability in simulation and real-world tests.
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Decoding High-Dimensional Finger Motion from EMG Using Riemannian Features and RNNs
TRR combines multi-band Riemannian features with a GRU to decode high-dimensional finger kinematics from EMG, achieving 9.79° intra-subject and 16.71° cross-subject average absolute error while running at ~10 Hz on a Raspberry Pi.
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One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation
A unified parameter space and canonical URDF enable cross-embodiment dexterous grasping policies with 81.9% zero-shot success on unseen hands like the 3-finger LEAP Hand.