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Tactile sensing is critical for humans to perform everyday tasks. Whilesignificant progress has been made in analyzing object grasping from vision, itremains unclear how we can utilize tactile sensing to reason about and modelthe dynamics of hand-object interactions. In this work, we employ ahigh-resolution tactile glove to perform four different interactive activitieson a diversified set of objects. We build our model on a cross-modal learningframework and generate the labels using a visual processing pipeline tosupervise the tactile model, which can then be used on its own during the testtime. The tactile model aims to predict the 3d locations of both the hand andthe object purely from the touch data by combining a predictive model and acontrastive learning module. This framework can reason about the interactionpatterns from the tactile data, hallucinate the changes in the environment,estimate the uncertainty of the prediction, and generalize to unseen objects.We also provide detailed ablation studies regarding different system designs aswell as visualizations of the predicted trajectories. This work takes a step ondynamics modeling in hand-object interactions from dense tactile sensing, whichopens the door for future applications in activity learning, human-computerinteractions, and imitation learning for robotics.

Dynamic Modeling of Hand-Object Interactions via Tactile Sensing