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SPIDER: Scalable Physics-Informed Dexterous Retargeting

arXiv — cs.CV•Thursday, November 13, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
On November 13, 2025, the SPIDER framework was unveiled, offering a solution to the data scarcity problem in robotics by utilizing abundant human motion data from various sources like motion capture and virtual reality. This innovative approach allows for the transformation of kinematic human demonstrations into dynamically feasible robot trajectories, significantly improving success rates by 18% compared to standard sampling methods. SPIDER operates at a remarkable speed, being 10 times faster than traditional reinforcement learning baselines, which enhances its practicality for real-world applications. The framework's scalability is evident as it effectively operates across nine different humanoid and dexterous hand embodiments, utilizing six diverse datasets. This capability culminates in the generation of a massive dataset comprising 2.4 million frames, facilitating advanced policy learning and further pushing the boundaries of robotic dexterity and agility.
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