TTP: Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation

Model Checkpoints and Datasets

Download models from Hugging Face:

Model Type	Model Name	Description
VLA Pre-trained	TTP-pretrained	Base vision-language-action model (preview)
VLA Post-trained	TTP-LIBERO	Post-trained on LIBERO benchmark
VLA Post-trained	TTP-LIBERO-plus	Post-trained on LIBERO, zero-shot evaluated on LIBERO-plus
VLA Post-trained	TTP-RoboCasa	Post-trained on RoboCasa 24 tasks

The whole datasets of H-Tac will be coming soon. For now, you can download a small subset of H-Tac from Hugging Face for testing and evaluation: H-Tac_Sample

Setup

Clone repository

git clone https://github.com/BeingBeyond/TTP.git
cd TTP

Create environment

conda create -n beingh python=3.10
conda activate beingh

Install package

pip install -r requirements.txt
pip install flash-attn --no-build-isolation

Pre-Training

bash scripts/train/train_human_tactile.sh

Post-Training

bash scripts/train/train_libero_example.sh
bash scripts/train/train_robocasa.sh

Evaluation

bash scripts/eval/eval-libero-fast.sh
bash scripts/eval/eval-libero-plus.sh
bash scripts/eval/eval-robocasa-multiprocess.sh

Acknowledgments

TTP builds on the following excellent open-source projects:

• InternVL: Vision-Language model backbone
• Bagel: Training framework
• Qwen: Language model and MoE expert
• LIBERO: Benchmark for lifelong robot learning
• RoboCasa: Large-scale simulation benchmark for everyday tasks
• Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization

We thank the authors for their contributions to the robotics and machine learning communities.

Citation

If you find this work useful in your research, please consider citing us!

@article{beingbeyond2026ttp,
  title={Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation},
  author={Zhang, Chi and Cai, Penglin and Xi, Ziheng and Yuan, Haoqi and Luo, Hao and Zhang, Wanpeng and Zheng, Sipeng and Xu, Chaoyi and Lu, Zongqing},
  journal={arXiv preprint arXiv:2607.01067},
  year={2026}
}