Robot AI trained on 4,983 hours of tactile records, evaluated on 26 real-world tasks

Robbyant released LingBot-Touch, a robot AI model that converts signals from magnetic, piezoresistive, and tactile sensors into a shared format, trained on 4,983 hours of tactile recordings totaling roughly 1.3 billion frames. The work, detailed in a preprint submitted to arXiv on the 10th at 3:12 a.m. KST, has not yet been peer-reviewed or accepted by a journal. The model uses sensor-specific tokenizers feeding a common geometric interface and a shared Haptic Transformer, addressing the difficulty of transferring data across sensors that produce fundamentally different signals. It matters because a unified tactile representation could let one model generalize across heterogeneous hardware, a longstanding barrier to scaling robot learning. Next, watch for peer review and independent evaluation on the 26 real-world manipulation tasks referenced in the title.