README.md

---
library_name: lerobot
license: apache-2.0
language:
  - en
base_model:
  - SberRoboticsCenter/GreenVLA-5b-base-stride-1
pipeline_tag: robotics
tags:
  - robotics
  - vla
  - vision-language-action
  - manipulation
  - flow-matching
  - action-prediction
  - green-vla
  - bridge
  - widowx
datasets:
  - IPEC-COMMUNITY/bridge_orig_lerobot
model-index:
  - name: GreenVLA-5b-stride-1-R1-bridge
    results:
      - task:
          type: robotics
          name: SimplerEnv WidowX (Bridge)
        dataset:
          type: IPEC-COMMUNITY/bridge_orig_lerobot
          name: Bridge
        metrics:
          - type: success_rate
            name: Partial Average
            value: 89.6
          - type: success_rate
            name: Entire Average
            value: 72.9
---

<div align="center">

# GreenVLA-5b-stride-1-R1-bridge

### Embodiment-Adapted VLA for Bridge (WidowX)

**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-1-R1-bridge** is the R1 (embodiment-adapted) checkpoint of the [Green-VLA](https://arxiv.org/abs/2602.00919) family, fine-tuned on the [Bridge](https://huggingface.co/datasets/IPEC-COMMUNITY/bridge_orig_lerobot) dataset for the WidowX robot arm.

Starting from the [GreenVLA-5b-base-stride-1](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-base-stride-1) pretrained checkpoint, this model was adapted via supervised fine-tuning (R1 stage) to the Bridge embodiment, achieving strong manipulation performance on the SimplerEnv benchmark.

## Evaluation

Evaluated on **SimplerEnv WidowX (Bridge)** benchmark with default episode length.

> **Note:** Bridge benchmark results can vary up to ±6% between runs. We recommend averaging over multiple evaluation runs for reliable comparisons.

### Partial Success Rate

| Task | Success Rate |
|------|:---:|
| Put Spoon on Towel | 91.7% |
| Put Carrot on Plate | 75.0% |
| Stack Blocks | 91.7% |
| Put Eggplant in Basket | 100.0% |
| **Average** | **89.6%** |

### Entire Success Rate

| Task | Success Rate |
|------|:---:|
| Put Spoon on Towel | 79.2% |
| Put Carrot on Plate | 62.5% |
| Stack Blocks | 58.3% |
| Put Eggplant in Basket | 91.7% |
| **Average** | **72.9%** |

## Training

| | Details |
|---|---|
| **Base checkpoint** | [GreenVLA-5b-base-stride-1](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-base-stride-1) |
| **Stage** | R1 — Embodiment-specific adaptation |
| **Method** | Supervised fine-tuning |
| **Dataset** | [IPEC-COMMUNITY/bridge_orig_lerobot](https://huggingface.co/datasets/IPEC-COMMUNITY/bridge_orig_lerobot) |
| **Robot** | WidowX (Bridge) |
| **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-1-R1-bridge",
    data_config_name="bridge",
)
policy.to("cuda").eval()

# 2. Build an observation (replace with real sensor data).
raw_obs = {
    "observation/state": np.random.rand(8).astype(np.float32),  # x y z roll pitch yaw _pad_ gripper
    "observation/image": np.random.randint(0, 256, size=(224, 224, 3), dtype=np.uint8),
    "prompt": "pick up the green block and place it on the plate",
}

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

## 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>