DexCanvas: Bridging Human Demonstrations and Robot Learning for Dexterous Manipulation
This dataset addresses a bottleneck for researchers in robotic manipulation learning, contact-rich control, and skill transfer, though it is incremental as it builds on existing taxonomies and simulation methods.
The authors tackled the lack of large-scale, physics-validated datasets for dexterous robotic manipulation by creating DexCanvas, a hybrid real-synthetic dataset with 7,000 hours of hand-object interactions across 21 manipulation types, derived from 70 hours of real human demonstrations and including synchronized multi-view RGB-D, mocap, and contact force data.
We present DexCanvas, a large-scale hybrid real-synthetic human manipulation dataset containing 7,000 hours of dexterous hand-object interactions seeded from 70 hours of real human demonstrations, organized across 21 fundamental manipulation types based on the Cutkosky taxonomy. Each entry combines synchronized multi-view RGB-D, high-precision mocap with MANO hand parameters, and per-frame contact points with physically consistent force profiles. Our real-to-sim pipeline uses reinforcement learning to train policies that control an actuated MANO hand in physics simulation, reproducing human demonstrations while discovering the underlying contact forces that generate the observed object motion. DexCanvas is the first manipulation dataset to combine large-scale real demonstrations, systematic skill coverage based on established taxonomies, and physics-validated contact annotations. The dataset can facilitate research in robotic manipulation learning, contact-rich control, and skill transfer across different hand morphologies.