Dantong Niu团队开源了T-Rex，一个在触觉反应式灵巧操作方面取得显著成果的项目，数据集和模型都很有价值，值得一试。
T-Rex项目由Dantong Niu领导，采用100小时触觉同步灵巧操作数据集，包含7,700+轨迹、22个运动原语和200+日常物体。采用触觉反应MoT架构，实现空间时间触觉编码和异步高频触觉细化。在12个真实世界接触密集型操作任务中，T-Rex比最强基线平均成功率高30%以上。数据集、模型、远程操作堆栈、训练代码和推理管道全部开源。
T-Rex: Tactile-Reactive Dexterous Manipulation Website: https://t.co/3irVptK79y Open dataset: https:...
T-Rex: Tactile-Reactive Dexterous Manipulation Website: tactile-reactive-dexterous.github.io Open dataset: huggingface.co/datasets/zekai… This work is led by @Dantong_Niu and co-advised by @trevordarrell . Congrats to the team! x.com/Dantong_Niu/st… Dantong Niu @Dantong_Niu Excited to share T-Rex: Tactile-Reactive Dexterous Manipulation 🦖🤖 Touch is fundamental to human dexterity, yet most Vision-Language-Action (VLA) models either ignore tactile feedback or lack the ability to react to high-frequency contact signals. In this work, we tackle both the data and architectural challenges of tactile-reactive dexterous manipulation. 🦖 A 100-hour tactile-synchronized dexterous manipulation dataset with 7,700+ trajectories, 22 motor primitives, and 200+ everyday objects. 🦖 A tactile-reactive MoT architecture with spatial-temporal tactile encoding and asynchronous high-frequency tactile refinement. 🦖 A scalable training recipe combining 22,889 hours of human egocentric pretraining with tactile-grounded robot mid-training. Across 12 real-world contact-rich manipulation tasks, T-Rex achieves over 30% higher average success rate than the strongest baseline. We are fully open-sourcing the dataset, models, teleoperation stack, training code, and inference pipeline. 🌐 Pro tactile-rex.github.io RR8YXU 📄 arxiv.org/abs/2606.17055 2UNLlqc � github.com/ZhuoyangLiu200… kCxUtwKC 🤗 huggingface.co/datasets/zekai… NwW8dcRZL 🧵 Thread ↓ Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 1 🔄 3 ❤️ 12 👀 2898 📊 3 ⚡