--- license: cc-by-sa-4.0 library_name: n0vtla tags: - robotics - vision-language-action - tactile - manipulation --- # N0-VTLA - NeoSim Grasp Chip 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 | Single-task policy for the NeoSim `grasp_chip` task. **100%**, first attempt. ### Serving `action_horizon` is **16**, which differs from the config default, so it must be overridden: ```bash VTLA_ASSET_ID=univtac_grasp_chip_joint_h16_norm \ python scripts/serve_zmq.py --config sim_single_arm_tactile \ --ckpt --action-horizon 16 --addr "tcp://127.0.0.1:5557" ``` Set `exec_horizon: 16` in the deploy YAML to match. Prompt: `grasp chip` ## 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.