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.
Dexgraspnet: A large-scale robotic dexterous grasp dataset for general objects based on simulation
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2026 5representative citing papers
SECOND-Grasp integrates semantic contact proposals from vision-language reasoning with geometric refinement to achieve 98%+ lifting success and improved intent-aware grasping on seen and unseen objects.
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.
CoLA-Flow Policy encodes action sequences into latent trajectories and performs flow matching there, yielding near-single-step inference with up to 93.7% smoother trajectories and 25-point higher success rates than raw-action flow baselines.
A structured survey of dexterous robotic hand research that reviews hardware, control methods, data resources, and benchmarks while identifying major limitations and future directions.
citing papers explorer
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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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SECOND-Grasp: Semantic Contact-guided Dexterous Grasping
SECOND-Grasp integrates semantic contact proposals from vision-language reasoning with geometric refinement to achieve 98%+ lifting success and improved intent-aware grasping on seen and unseen objects.
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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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CoLA-Flow Policy: Temporally Coherent Imitation Learning via Continuous Latent Action Flow Matching for Robotic Manipulation
CoLA-Flow Policy encodes action sequences into latent trajectories and performs flow matching there, yielding near-single-step inference with up to 93.7% smoother trajectories and 25-point higher success rates than raw-action flow baselines.
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Towards Robotic Dexterous Hand Intelligence: A Survey
A structured survey of dexterous robotic hand research that reviews hardware, control methods, data resources, and benchmarks while identifying major limitations and future directions.