README.md

---
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
library_name: n0vtla
tags:
  - robotics
  - vision-language-action
  - tactile
  - simulation
---

# 𝒩₀-VTLA — NeoSim dual-arm

A 𝒩₀-VTLA dual-arm policy for the [NeoSim](https://github.com/anlorla/UniVTAC/tree/NeoSim) simulator,
post-trained from the [𝒩₀-VTLA pretrained base](https://huggingface.co/NeoteAI/n0-vtla-base).
This card documents the `dual_bowl_place_stack` setup.

| | |
|---|---|
| Embodiment | dual-arm |
| Action space | joint, 16-dim (2 x (7 joints + gripper)) |
| Action horizon | 16 |
| Tactile views | 4 |
| Latent tactile tokens | 5 |

## Files

```
model.safetensors                                     the full checkpoint (~8.25 GB)
assets/n0_dual_bowl_place_stack_norm/norm_stats.json
config.json                                           architecture summary
```

## Serve

The evaluation adapter speaks ZMQ + msgpack, so use `scripts/serve_zmq.py` from the
[code repository](https://github.com/neoteai/N0-VTLA):

```bash
hf download NeoteAI/n0_VTLA_dual_bowl_place_stack --local-dir checkpoints/n0_VTLA_dual_bowl_place_stack

VTLA_ASSET_ID=n0_dual_bowl_place_stack_norm python scripts/serve_zmq.py \
  --config sim_dual_arm_tactile \
  --ckpt checkpoints/n0_VTLA_dual_bowl_place_stack \
  --addr "tcp://*:5557" \
  --default-prompt "Use both arms to place and stack the bowls"
```

Serving reads only `model.safetensors` and `assets/<asset-id>/norm_stats.json`; no dataset is
needed. A correct load prints `tactile=True` with 4 views and no missing or unexpected
state-dict keys. If it reports either, the config does not match the checkpoint.

Then run the evaluation from a NeoSim checkout with a deploy YAML pointing at port 5557. Set
`exec_horizon` to 16, the model's action horizon: executing fewer steps clips the tail of each
chunk, which is where the grasp-closing motion lives.

## Action space

Unlike the pretrained base, which predicts end-effector deltas in a canonical 32-dim rot6d
container, this policy predicts **joint** actions: 7 joints plus gripper per arm. The joint dims
are element-wise deltas against the current state; the gripper columns are absolute. Do not feed
it end-effector data or reuse an end-effector normalization asset.

## License

These weights are derived from Google's PaliGemma/Gemma parameters and are made available under
the [Gemma Terms of Use](https://ai.google.dev/gemma/terms) and the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy), not under the
CC BY-SA 4.0 licence that covers the source code. This is inherited from the base model.