PanTact: Teaching Whole-Body Dexterity with a Wearable Contact Interface
PanTact treats contact, not joint trajectories, as the transfer currency from people to humanoids. Wearable patches are assembled magnetically from 44x44 mm FSR modules; a custom 50x50 mm PCB streams 16 channels at 1 kHz over a CRC-checked RS-485 daisy chain, one Vive tracker per patch maps sensor centers into the world frame, and contact states are binarized against a per-channel adaptive baseline with threshold max(0.35 beta, r_min). The policy observes body-masked 3D egocentric point clouds, both wrist images and proprioception, and at 10 Hz predicts an action chunk of H=16 steps containing a look-at point, EEF poses with gripper widths, and per-sensor contact goals (binary state plus 3D position). A soft-minimum proximity d(p,q) with T = 0.02 m hands each active contact to whichever robot surface is currently nearest, and active goals are offset by delta along the pressing direction to form virtual goals that generate pressing force, so the G1 holds objects without any tactile sensor on its body. Three control formulations trade off differently: training-free upper-body IK, WBIK whose dense reference is executed by SONIC, and a sparse contact tracker trained with PPO over 4096 simulation environments for 30k iterations (50 Hz, 29 joints, S=8 upper-body / S=16 whole-body contact slots). On a 29-DoF Unitree G1: Grasp-and-Embrace reaches 85% with upper-body IK and 80% with the sparse tracker against 0% for Humanoid UMI; Sweep-and-Stow reaches 90.63% end to end while Humanoid UMI++ scores 18.75% on each stage and 0% end to end; Carry-and-Push is 90% versus 40%. In simulation replay, adding contact goals lowers contact distance for all three controllers at almost no cost in EEF accuracy.
