GreenVLA-2b-base

Staged Vision-Language-Action Model for Generalist Robots

Sber Robotics Center · Manipulation Team

---

Overview

GreenVLA-2b-base is the lightweight base checkpoint of the Green-VLA family — a ~2B-parameter Vision-Language-Action model pretrained on both general-domain and robotics data (3,000+ hours of demonstrations across multiple embodiments).

This checkpoint combines:

• VLM capabilities — Visual Question Answering, object pointing, bounding box prediction, and scene description.
• Autoregressive action prediction — FAST token-based action generation for discrete control.
• Flow-matching action expert — A continuous action head for smooth, high-frequency trajectory generation.

Use this checkpoint when you need a smaller model footprint for fine-tuning or deployment on resource-constrained hardware. For best performance, consider GreenVLA-5b-base.

Architecture

Training Curriculum

This checkpoint corresponds to the Base stage of the Green-VLA curriculum:

Quick Start

Installation

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

Action Inference

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-2b-base",
    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 for the full runnable script with argument parsing.

VLM Inference (VQA, Pointing, BBox)

The base model retains full VLM capabilities:

from PIL import Image
from lerobot.common.policies.factory import load_pretrained_policy

# Load without data transforms
policy, _, _ = load_pretrained_policy(
    "SberRoboticsCenter/GreenVLA-2b-base",
    data_config_name=None,
)
policy = policy.to("cuda").eval()

# Access the processor and model directly
processor = policy.model.processor
image = Image.open("scene.jpg")

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "Describe what the robot should do next."},
        ],
    }
]

inputs = processor.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=False,
    return_dict=True, return_tensors="pt",
    padding_side="left", padding="max_length", max_length=256,
    images_kwargs={"do_resize": True},
).to("cuda")

generated_ids = policy.model.model.generate(
    **inputs, max_new_tokens=256, do_sample=False, use_cache=False,
)

generated_ids_trimmed = [
    out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)
]
print(processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True)[0])

Model Family

Citation

@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},
}

© 2026 Sber Robotics Center · Manipulation Team