用3万小时触觉数据补齐具身智能「手感」，新智具身联合复旦大学统一触觉「方言」

TL;DR - NeoteAI and Fudan University released three technical reports and open-source resources that make tactile sensing a core input for robot learning. Their approach unifies heterogeneous sensors and uses predicted touch signals to improve fine manipulation and world-model planning.

• NeoData contains over 30,000 hours of visual-tactile interactions across 450 tasks and six robot platforms; 5,000 hours are open-sourced.
• NeoForce learns a transferable tactile representation across sensor formats such as deformation images, capacitance matrices, and force vectors.
• Neo-VTLA predicts tactile changes up to 50 steps ahead and reportedly improves plug insertion, key removal, and long-horizon manipulation.
• The 7.2B-parameter Neo-TWAM jointly predicts future video, touch, and actions, outperforming cited world-model baselines in simulation and eight real-robot tasks.