README.md

---
library_name: lerobot
license: apache-2.0
language:
  - en
base_model:
  - SberRoboticsCenter/GreenVLA-5b-base-stride-4
pipeline_tag: robotics
tags:
  - robotics
  - vla
  - vision-language-action
  - manipulation
  - flow-matching
  - action-prediction
  - green-vla
  - fractal
  - google-robot
datasets:
  - IPEC-COMMUNITY/fractal20220817_data_lerobot
model-index:
  - name: GreenVLA-5b-stride-4-R1-fractal
    results:
      - task:
          type: robotics
          name: SimplerEnv Google Robot (Fractal)
        dataset:
          type: IPEC-COMMUNITY/fractal20220817_data_lerobot
          name: Fractal
        metrics:
          - type: success_rate
            name: Matching Average
            value: 77.0
          - type: success_rate
            name: Variant Average
            value: 66.7
          - type: success_rate
            name: Overall Average
            value: 71.8
---

<div align="center">

# GreenVLA-5b-stride-4-R1-fractal

### Embodiment-Adapted VLA for Fractal (Google Robot)

**Sber Robotics Center &middot; Manipulation Team**

[![arXiv](https://img.shields.io/badge/arXiv-2602.00919-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.00919)
[![Project Page](https://img.shields.io/badge/Project-Page-blue?style=for-the-badge&logo=github&logoColor=white)](https://greenvla.github.io/)
[![Code](https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/greenvla/GreenVLA)

</div>

---

## Overview

**GreenVLA-5b-stride-4-R1-fractal** is the R1 (embodiment-adapted) checkpoint of the [Green-VLA](https://arxiv.org/abs/2602.00919) family, fine-tuned on the [Fractal](https://huggingface.co/datasets/IPEC-COMMUNITY/fractal20220817_data_lerobot) dataset for the Google Robot.

Starting from the [GreenVLA-5b-base-stride-4](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-base-stride-4) pretrained checkpoint, this model was adapted via supervised fine-tuning (R1 stage) to the Fractal embodiment, achieving strong manipulation performance on the SimplerEnv benchmark.

## Evaluation

Evaluated on **SimplerEnv Google Robot (Fractal)** benchmark with default episode length:

### Visual Matching

| Task | Success Rate |
|------|:---:|
| Coke Can | 85.7% |
| Move Near | 75.8% |
| Drawer | 64.8% |
| Apple in Drawer | 81.5% |
| **Average** | **77.0%** |

### Variant Aggregation

| Task | Success Rate |
|------|:---:|
| Coke Can | 92.6% |
| Move Near | 71.9% |
| Drawer | 35.7% |
| Apple in Drawer | 66.7% |
| **Average** | **66.7%** |

### Overall Average: **71.8%**

## Training

| | Details |
|---|---|
| **Base checkpoint** | [GreenVLA-5b-base-stride-4](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-base-stride-4) |
| **Stage** | R1 — Embodiment-specific adaptation |
| **Method** | Supervised fine-tuning |
| **Dataset** | [IPEC-COMMUNITY/fractal20220817_data_lerobot](https://huggingface.co/datasets/IPEC-COMMUNITY/fractal20220817_data_lerobot) |
| **Robot** | Google Robot (Fractal) |
| **Parameters** | ~5B |

## Quick Start

### Installation

```bash
git clone https://github.com/greenvla/GreenVLA.git
cd GreenVLA
uv sync  # or: pip install -e .
```

### Inference

```python
import numpy as np
import torch
from lerobot.common.policies.factory import load_pretrained_policy
from lerobot.common.utils.torch_observation import (
    move_dict_to_batch_for_inference,
    torch_preprocess_dict_inference,
)

# 1. Load policy and transforms.
policy, input_transforms, output_transforms = load_pretrained_policy(
    "SberRoboticsCenter/GreenVLA-5b-stride-4-R1-fractal",
    data_config_name="fractal",
)
policy.to("cuda").eval()

# 2. Build an observation (replace with real sensor data).
raw_obs = {
    "observation/state": np.random.rand(8),  # x, y, z, rx, ry, rz, rw, gripper
    "observation/image": np.random.randint(256, size=(448, 448, 3), dtype=np.uint8),
    "prompt": "move the coke can to the left of the table",
}

# 3. Transform, preprocess, and batch.
obs = input_transforms(raw_obs)
obs = torch_preprocess_dict_inference(obs)
batch = move_dict_to_batch_for_inference(obs, device="cuda")

# 4. Predict actions and post-process.
with torch.inference_mode():
    raw_actions = policy.select_action(batch).cpu().numpy()

actions = output_transforms(
    {"actions": raw_actions, "state": batch["state"].cpu().numpy()}
)["actions"]
# actions shape: (action_horizon, 7) — [x, y, z, roll, pitch, yaw, gripper]
```

See [`examples/example_inference_fractal.py`](https://github.com/greenvla/GreenVLA/blob/main/examples/example_inference_fractal.py) for the full runnable script with argument parsing.

> **Note:** The Fractal embodiment uses an 8-dim proprioceptive state `[x, y, z, rx, ry, rz, rw, gripper]` and `data_config_name="fractal"` — this differs from Bridge which uses `data_config_name="bridge"` and a different state layout.

## Citation

```bibtex
@misc{apanasevich2026greenvlastagedvisionlanguageactionmodel,
    title   = {Green-VLA: Staged Vision-Language-Action Model for Generalist Robots},
    author  = {I. Apanasevich and M. Artemyev and R. Babakyan and P. Fedotova and
               D. Grankin and E. Kupryashin and A. Misailidi and D. Nerus and
               A. Nutalapati and G. Sidorov and I. Efremov and M. Gerasyov and
               D. Pikurov and Y. Senchenko and S. Davidenko and D. Kulikov and
               M. Sultankin and K. Askarbek and O. Shamanin and D. Statovoy and
               E. Zalyaev and I. Zorin and A. Letkin and E. Rusakov and
               A. Silchenko and V. Vorobyov and S. Sobolnikov and A. Postnikov},
    year    = {2026},
    eprint  = {2602.00919},
    archivePrefix = {arXiv},
    primaryClass  = {cs.RO},
    url     = {https://arxiv.org/abs/2602.00919},
}
```

<div align="center">

&copy; 2026 Sber Robotics Center &middot; Manipulation Team

</div>