DexSeed: Expanding Human Hand-Object Interactions into Robot Demonstrations for Dexterous Policy Learning
Overview
This research presents DexSeed, a framework for low-cost generation of dexterous robot data from human hand-object interactions. The method organizes source sequences by grasp mode, uses InteractionMesh-based retargeting and arm trajectory augmentation to adapt sequences to robot hands, object geometries, and locations while preserving local relationships. An RL finger-control policy produces executable rollouts, and rendered observations paired with grasp descriptions fine-tune a VLA. Experiments show improved contact fidelity and reliable whole-arm execution in simulation and real-world deployment, enabling the execution of language-specified grasps using only generated demonstrations.
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Environment: simulation
“ns under arm-tracking dynamics in physics simulation. Specifically, we organize source sequences by grasp mode with shared language descriptions. InteractionMesh-based retargeting and arm trajectory augmentation adapt these sequences to the robot hand, different object geometries, and pick-and-place locations while preserving local hand--object relationships. An RL finger-control policy produces executable rollouts,”
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- arXiv Robotics — research abstractsSignalDexSeed: Expanding Human Hand-Object Interactions into Robot Demonstrations for Dexterous Policy Learning
This research presents DexSeed, a framework for low-cost generation of dexterous robot data from human hand-object interactions. The method organizes source sequences by grasp mode, uses InteractionMesh-based retargeting and arm trajectory augmentation to adapt sequences to robot hands, object geometries, and locations while preserving local relationships. An RL finger-control policy produces executable rollouts, and rendered observations paired with grasp descriptions fine-tune a VLA. Experiments show improved contact fidelity and reliable whole-arm execution in simulation and real-world deployment, enabling the execution of language-specified grasps using only generated demonstrations.
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