Presenting research from @berkeley_ai Humanoid Intelligence Center. DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation.🖐️🤖 Dexterous manipulation requires touch, yet multi-finger tactile data remain scarce and expensive to collect at scale. Through continual vision-to-touch learning, DexTacWAM adapts a pretrained video world model into a visuo-tactile world model for predictive multi-finger contact modeling and action generation. Across six contact-rich dexterous manipulation tasks, DexTacWAM achieves the highest score on every task, averaging 70.6 vs. 38.0 for the strongest baseline. 🔬 Why not simply inject tactile features into the action policy? With the same multi-finger tactile encoder, policy architecture, and training procedure, replacing the tactile world-model latent with direct tactile features drops the 4-task mean from 74.7 to 26.6. Takeaway: the benefit does not come simply from providing tactile observations to the policy, but from making multi-finger contact evolution part of the predicted world state. All code, model weights, and datasets are now open-sourced! 🌐 Project: dextacwam.github.io/ 📄 Paper: arxiv.org/abs/2609.24976 💻 Code: github.com/dextacwam/DexTacW… 🤗 Weights&Data: huggingface.co/collections/J… Author List: @Jensen_Yuan, @zekaiw04, @sbn_epiphany Haoran Lu, @trevordarrell, @Ismini_L, @Wei_ZHAN_