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Touch2Robot, a framework that lets humans collect demonstrations while seeing how the target robot hand would contact the object, shows that bringing robot touch into the human demonstration loop improves both the quality and efficiency of scalable dexterous data collection.
Experiments show that joint pretraining on Ego2Robot-synthesized and robot data consistently improves out-of-distribution generalization across multiple perturbation types, with benefits validated on real-robot deployment.
This work introduces DexMani, a framework that transfers human demonstrations as contact-conditioned manipulability evolution and uses it to guide downstream reinforcement learning, enabling rotation skills to be acquired across robot embodiments with distinct kinematics and active-contact configurations.
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