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Tactus is a textile‑integrated glove that combines a thin, conformal 3D‑knit design with strategically placed capacitive tactile sensors and IMU‑fused pose tracking to capture natural human manipulation. It offers a calibrated, cross‑glove consistency pipeline and per‑session subject correction, achieving a minimum detectable force of 0.59 N and fingertip pose error of 11.0 mm even under heavy occlusion. The authors benchmark Tactus against two commercial gloves, presenting the first quantitative cross‑platform evaluation of tactile force regression and hand pose estimation.
arXiv:2608.15060v2 Announce Type: replace Abstract: Touch is fundamental to dexterous manipulation, yet most egocentric human data increasingly used for robot learning lacks tactile information. Dire...
TACROSS is a low‑cost, scalable system that enables robots to learn from human touch by aligning tactile streams at the level of contact events rather than raw sensor values. It combines a $10.86 piezoresistive glove with 285 sensing points and a temporal Transformer that maps heterogeneous signals into a shared 256‑dimensional latent space. The approach uses robot demonstrations for action supervision while human demonstrations aid tactile representation learning, achieving a 3.5‑fold efficiency improvement and a 95.7% reduction in demonstration equipment cost across four manipulation tasks.
ForceFlow is a force-aware reactive framework that uses flow matching to improve contact-rich manipulation. It fuses force signals asymmetrically, treats force as a global regulator, and employs a joint prediction paradigm to couple force and motion. The approach splits tasks into a vision-dominant localization stage and a touch-dominant execution stage, using a Vision-to-Force handover to separate spatial generalization from contact regulation.
arXiv:2606. 24712v1 Announce Type: cross Abstract: Humans effortlessly locate and identify objects by touch alone, even without vision.
DexTouch-WM is an action‑conditioned world model that learns from scalable human touch to predict future RGB observations and bilateral tactile dynamics for dexterous robot manipulation. By using compatible piezoresistive arrays on both human and robot hands and retargeting human motion into the robot action space, the model can be supervised with human interaction data while keeping a fixed amount of real‑robot supervision. Experiments show that adding up to 100 hours of human interaction improves robot‑domain visual, geometric, and contact prediction, and the model can serve as a surrogate environment for policy evaluation and synthetic trajectory generation.