| --- |
| license: cc-by-sa-4.0 |
| library_name: n0vtla |
| tags: |
| - robotics |
| - vision-language-action |
| - tactile |
| - manipulation |
| --- |
| |
| # N0-VTLA - NeoSim Plug USB |
|
|
| Task policy for [N0-VTLA](https://github.com/neoteai/N0-VTLA), a vision-tactile-language-action |
| model that conditions a flow-matching action expert on predicted latent tactile tokens. |
|
|
| This is a **task policy, not a pretrained base**. For post-training on your own robot start from |
| [n0-vtla-base](https://huggingface.co/NeoteAI/n0-vtla-base). |
|
|
| | | | |
| |---|---| |
| | Config | `sim_single_arm_tactile` | |
| | Tactile pathway | enabled, `n_latent=5`, views `(tactile_a, tactile_b)` | |
| | Action space | 8-dim joint | |
|
|
| Policy for the NeoSim `insert_USB` task. |
|
|
| **80%**, first attempt. |
|
|
| ### Serving |
|
|
| ```bash |
| VTLA_ASSET_ID=univtac_single4_joint_norm \ |
| python scripts/serve_zmq.py --config sim_single_arm_tactile \ |
| --ckpt <this-dir> --addr "tcp://127.0.0.1:5557" |
| ``` |
|
|
| `action_horizon` is 50; set `exec_horizon: 50`. Prompt: `insert USB` |
|
|
| ## Evaluation protocol |
|
|
| Measured on the UniVTAC simulator at commit `695a22d` |
| (branch `NeoSim` of [anlorla/UniVTAC](https://github.com/anlorla/UniVTAC)), on held-out seeds |
| starting at 100. Success criteria on three tasks were tightened after these numbers were |
| measured, so a success rate on this benchmark is not comparable without the simulator commit |
| beside it; see |
| [docs/EVAL.md](https://github.com/neoteai/N0-VTLA/blob/main/docs/EVAL.md) for the details and for |
| the full evaluation procedure. |
|
|
| ## Caveat on the tactile pathway |
|
|
| This checkpoint carries the tactile pathway, but this benchmark cannot demonstrate that touch |
| contributes to the score. Object randomisation is +/-2-5 mm with no domain randomisation, so a |
| policy that ignores its cameras and its tactile sensors entirely can still score well. Use |
| `scripts/probe_z_tactile_dependence.py` to measure the causal contribution yourself. |
|
|
| ## Citation |
|
|
| If you use this model in your research, please cite the [N0-VTLA paper](https://arxiv.org/abs/2607.23782): |
|
|
| ```bibtex |
| @misc{neoteaiteam2026n0vtlascalingvisiontactilelanguageactionmodel, |
| title={$N_0$-VTLA: Scaling Vision-Tactile-Language-Action Model with Latent Tactile Tokens}, |
| author={NeoteAI Team and Fudan TEAI Team}, |
| year={2026}, |
| eprint={2607.23782}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.RO}, |
| url={https://arxiv.org/abs/2607.23782}, |
| } |
| ``` |
|
|
| ## License |
|
|
| CC BY-SA 4.0, as the parent repository. |
|
|
