伯克利发布灵巧操作触觉视觉模型DexTacWAM
伯克利团队开源了DexTacWAM模型，它在灵巧操作任务上比最强基线高出近一倍，代码和模型权重已全部公开。
加州大学伯克利分校人工智能中心发布了DexTacWAM模型，这是一个用于灵巧操作的视觉-触觉世界-行动模型。该模型通过持续的视觉到触觉学习，将预训练的视频世界模型适应为预测多指接触建模和行动生成的视觉-触觉世界模型。在六项接触密集的灵巧操作任务中，DexTacWAM在每项任务上都取得了最高分，平均得分为70.6，而最强基线模型的平均得分为38.0。
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! 🌐 Proje dextacwam.github.io CO4f 📄 Pa arxiv.org/abs/2609.24976 P1RYu 💻 github.com/dextacwam/DexT… UCB0fj 🤗 Weights huggingface.co/collections/Je… kKWShNE Author List: @Jensen_Yuan , @zekaiw04 , @sbn_epiphany Haoran Lu, @trevordarrell , @Ismini_L , @Wei_ZHAN_ Your browser does not support the video tag. 🔗 View on Twitter 💬 3 🔄 1 ❤️ 11 👀 1979 📊 5 ⚡
