---
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.