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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.
## License
CC BY-SA 4.0, as the parent repository.
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