@DrJimFan
T-Rex: Tactile-Reactive Dexterous Manipulation Website: https://t.co/3irVptK79y Open dataset: https://t.co/iKJrMVrfGm This work is led by @Dantong_Niu and co-advised by @trevordarrell. Congrats to the team! https://t.co/i9fpN9XkPT
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"text": "T-Rex: Tactile-Reactive Dexterous Manipulation\nWebsite: https://t.co/3irVptK79y\nOpen dataset: https://t.co/iKJrMVrfGm\nThis work is led by @Dantong_Niu and co-advised by @trevordarrell. Congrats to the team! \n\nhttps://t.co/i9fpN9XkPT",
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"text": "Excited to share T-Rex: Tactile-Reactive Dexterous Manipulation 🦖🤖\n\nTouch 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.\n\nIn this work, we tackle both the data and architectural challenges of tactile-reactive dexterous manipulation.\n\n🦖 A 100-hour tactile-synchronized dexterous manipulation dataset with 7,700+ trajectories, 22 motor primitives, and 200+ everyday objects.\n\n🦖 A tactile-reactive MoT architecture with spatial-temporal tactile encoding and asynchronous high-frequency tactile refinement.\n\n🦖 A scalable training recipe combining 22,889 hours of human egocentric pretraining with tactile-grounded robot mid-training.\n\nAcross 12 real-world contact-rich manipulation tasks, T-Rex achieves over 30% higher average success rate than the strongest baseline.\n\nWe are fully open-sourcing the dataset, models, teleoperation stack, training code, and inference pipeline.\n\n🌐 Project: https://t.co/AiHKRR8YXU\n📄 Paper: https://t.co/mXY2UNLlqc\n💻 Code: https://t.co/7skCxUtwKC\n🤗 Dataset: https://t.co/uNwW8dcRZL\n🧵 Thread ↓",
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