Robbyant open-sources LingBot-Touch tactile foundation model

Robbyant has open-sourced LingBot-Touch, a tactile foundation model pretrained on about 5,000 hours of real tactile data that reads magnetic, vision-based tactile, and piezoresistive sensor inputs into a unified token space. Across 26 real robot tasks and two dexterous hands, four policies using these tokens all achieved the highest average success rates; on specific sensor models unseen during pretraining, a frozen encoder reduced contact-center error from 5.89 mm to 0.16 mm, outperforming an adapter built specifically for it.