3万小时触觉数据补齐具身智能“手感”！新智具身&复旦报告三连发

TL;DR - NeoteAI and Fudan University released three N0 technical reports, models, and part of a large vision-touch robotics dataset. The work positions tactile feedback as core infrastructure for more reliable physical manipulation.

• NeoData contains over 30,000 hours across 450 tasks and six robot platforms; 5,000 hours are open-sourced.
• NeoForce learns a unified tactile representation across different sensor types.
• N0-VTLA predicts tactile changes 50 steps ahead and uses failure data plus offline reinforcement learning to improve manipulation.
• N0-TWAM jointly predicts future video, touch, and actions, outperforming cited world-model baselines in simulation and real-robot tests.