Human hands play a central role in interacting, motivating increasing
research in dexterous robotic manipulation. Data-driven embodied AI algorithms
demand precise, large-scale, human-like manipulation sequences, which are
challenging to obtain with conventional reinforcement learning or real-world
teleoperation. To address this, we introduce ManipTrans, a novel two-stage
method for efficiently transferring human bimanual skills to dexterous robotic
hands in simulation. ManipTrans first pre-trains a generalist trajectory
imitator to mimic hand motion, then fine-tunes a specific residual module under
interaction constraints, enabling efficient learning and accurate execution of
complex bimanual tasks. Experiments show that ManipTrans surpasses
state-of-the-art methods in success rate, fidelity, and efficiency. Leveraging
ManipTrans, we transfer multiple hand-object datasets to robotic hands,
creating DexManipNet, a large-scale dataset featuring previously unexplored
tasks like pen capping and bottle unscrewing. DexManipNet comprises 3.3K
episodes of robotic manipulation and is easily extensible, facilitating further
policy training for dexterous hands and enabling real-world deployments.