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…
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