AutoDex automates the full perception-execution-labeling-reset loop for real-world dexterous grasping data collection, delivering 4.8x throughput over teleoperation and 76% success for retrieved grasps versus 34% from simulation-only data.
Springgrasp: Synthesizing com- pliant, dexterous grasps under shape uncertainty
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EAGG uses embodiment-specific graphs and iterative geometry injection in a shared generator to achieve 56.17% average success across six end-effectors on MultiGripperGrasp, within 1.10 pp of specialized models.
KPGrasp is a scalable Transformer flow-matching model using 3D hand keypoints that achieves 76.3% success on Dexonomy (47.4% improvement) and best average on DexGrasp Anything without contact losses or test-time refinement.
CoDex combines VLMs, constrained optimization, and RL to autonomously discover grasp-move-actuate policies for functional manipulation of unseen objects with internal mechanisms.
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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AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection
AutoDex automates the full perception-execution-labeling-reset loop for real-world dexterous grasping data collection, delivering 4.8x throughput over teleoperation and 76% success for retrieved grasps versus 34% from simulation-only data.
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EAGG: Embodiment-Aligned Grasp Generation via Geometry-Aware Graph Conditioning
EAGG uses embodiment-specific graphs and iterative geometry injection in a shared generator to achieve 56.17% average success across six end-effectors on MultiGripperGrasp, within 1.10 pp of specialized models.
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KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation
KPGrasp is a scalable Transformer flow-matching model using 3D hand keypoints that achieves 76.3% success on Dexonomy (47.4% improvement) and best average on DexGrasp Anything without contact losses or test-time refinement.
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CoDex: Learning Compositional Dexterous Functional Manipulation without Demonstrations
CoDex combines VLMs, constrained optimization, and RL to autonomously discover grasp-move-actuate policies for functional manipulation of unseen objects with internal mechanisms.
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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.