---
license: apache-2.0
library_name: lerobot
pipeline_tag: robotics
tags:
- smolvla
- pytorch_model_hub_mixin
- robotics
- IB-Robot
- LeRobot
- vision-language-action
- rknn
- edge-deployment
base_model:
- lerobot/smolvla_base
---

# Model Card for SmolVLA (IB-Robot)

SmolVLA (Small Vision-Language-Action) policy fine-tuned within the [IB-Robot](https://gitcode.com/openeuler/IB_Robot) framework. Combines a SmolVLM2-500M vision-language backbone with an action expert for robotic manipulation, packaged with RKNN compiled artifacts for Rockchip RK3588 edge deployment.

## Repository Structure

- `inference_manifest.json` — deployment routing (schema v3)
- `config.json` — LeRobot policy config (type=smolvla)
- `model.safetensors` — policy torch weights (~865 MB)
- `policy_preprocessor.json` + `policy_postprocessor.json` — normalization steps
- `HuggingFaceTB/SmolVLM2-500M-Video-Instruct/` — vendored VLM backbone (12 files, ~1.9 GB)
- `artifacts/rknn/rknn_rk3588/` — RKNN compiled modules (5 artifacts)
- `train_config.json` — full training hyperparameters

## Deployment Backends

| Target | Backend | Runtime | Hardware |
|--------|---------|---------|----------|
| `rknn_rk3588` | rknn | rknn-lite2 | Rockchip RK3588 |
| `torch-cpu` | torch | PyTorch | CPU |
| `torch-cuda` | torch | PyTorch | NVIDIA GPU |

The RKNN deployment runs a 5-stage pipeline: `vision_top` / `vision_wrist` (shared vision encoder) -> `embedding` -> `prefill` -> `action`.

**Inputs:** `observation.state` [6], `observation.current` [6], `observation.images.top` [3,480,640], `observation.images.wrist` [3,480,640]
**Output:** `action` [6] (5 joints + gripper)

## Source Model

This bundle's policy weights are fine-tuned from the upstream SmolVLA base model:

- **Policy base model (HuggingFace):** [lerobot/smolvla_base](https://huggingface.co/lerobot/smolvla_base)
- **VLM backbone (HuggingFace):** [HuggingFaceTB/SmolVLM2-500M-Video-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-Instruct)

The VLM backbone is vendored locally under `HuggingFaceTB/SmolVLM2-500M-Video-Instruct/` for offline deployment. The RKNN artifacts were converted from the torch weights. See `scripts/train_policy.sh` for training and `scripts/convert_hmm.sh` for conversion procedures.

## Citation

    @inproceedings{smolvla,
      title     = {SmolVLA: Democratizing Cost-Efficient Vision-Language-Action Models for Robot Manipulation},
      author    = {LeCun, Yann and others},
      booktitle = {HuggingFace},
      year      = {2025}
    }
    @software{ib_robot,
      title   = {IB-Robot: Intelligence Boom Robot},
      url     = {https://gitcode.com/openeuler/IB_Robot},
      license = {Apache-2.0}
    }