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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.
Tau is presented, a touch-augmented VLA framework that learns an action-conditioned spatiotemporal tactile representation from future visual supervision inspired by the Joint-Embedding Predictive Architecture (JEPA), and fuses it with vision-language features for action generation.
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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