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. 🌐 Project: tactile-rex.github.io/ 📄 Paper: arxiv.org/abs/2606.17055 💻 Code: github.com/ZhuoyangLiu2005/T… 🤗 Dataset: huggingface.co/datasets/zeka… 🧵 Thread ↓

Happy to share that 2 of our papers have been accepted to ICLR 2026 for the second year in a row 🦾 1️⃣ Learning to Grasp Anything by Playing with Random Toys in collaboration with @UCBerkeley and @PanasonicNA AI Labs. 🔗arxiv.org/abs/2510.12866 2️⃣ Video Unlearning via Low-Rank Refusal Vector, our newest work with @SapienzaPINlab. 🔗arxiv.org/abs/2506.07891 See you in Rio! 🇧🇷 #ICLR26