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README.md

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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.
+
+**16/20 (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
+
+Seeds 100-119, 20 episodes, one per seed, on the UniVTAC simulator at commit `695a22d`
+(branch `NeoSim` of [anlorla/UniVTAC](https://github.com/anlorla/UniVTAC)). Success criteria on
+three tasks were tightened after these numbers were measured; see
+[docs/EVAL.md](https://github.com/neoteai/N0-VTLA/blob/main/docs/EVAL.md) for the table and for
+why a success rate is meaningless without the simulator commit beside it.
+
+A single 20-episode run on this benchmark carries roughly +/-10 points of noise: repeating an
+identical evaluation on a fixed checkpoint has produced 60 / 30 / 50 on the same task.
+
+## 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.

assets/univtac_single4_joint_norm/norm_stats.json

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+{
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+        0.0
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+        0.0
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+    }
+  }
+}

model.safetensors

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