TouchTwin

Human demonstrations are becoming the main source of data for dexterous manipulation, and real-to-sim-to-real pipelines multiply each demonstration in simulation. These pipelines rebuild the interaction from vision, which recovers how the hand moves. Which fingers touch, when, and how hard remain unobserved, because a hand pose shows where each finger stops on the surface rather than how far it is driven to close. This gap is a force gap: a hand policy that imitates the observed pose grips held-out objects with 0.31× the recorded force, and a policy trained on physics replays of vision-tracked poses grips with 1.55×. TouchTwin closes the gap with measured human touch. Each selected TouchScale recording, captured with head and wrist RGB-D cameras and an 880-taxel glove, is rebuilt in MuJoCo. Coding agents write the objects as physics-tested programs, and a per-subject tactile hand replays the interaction with the recorded touch setting the contact and the closing force of each finger. In simulation, a policy trained on touch-grounded replays grips at 0.99× the recorded force and succeeds in 20/20 trials on held-out recordings and 35/36 unseen perturbations; a contact predictor trained on them misses 13% of real contact frames, against 32% for vision-based replays. Each replay also seeds perturbed demonstrations with simulated contact and force labels, rendered as realistic video in new views and scenes. Project page with videos: https://touchtwin.github.io.