spatialvla 4b 224 pt EU AI Act Compliance Profile

SpatialVLA

SpatialVLA is a spatial-enhanced vision-language-action model trained on 1.1 Million real robot episodes. The code is purely huggingFace-based and concise, with efficient performance.

All SpatialVLA checkpoints, as well as our training codebase are released under an MIT License.

For full details, please read our paper and see our project page.

Model Details

Model Description

• Developed by: The SpatialVLA team consisting of researchers from Shanghai AI Laboratory, ShanghaiTech and TeleAI.
• Model type: Vision-language-action (language, image => robot actions)
• Language(s) (NLP): en
• License: MIT
• Finetuned from model: paligemma2-3b-pt-224
• Pretraining Dataset: Open X-Embodiment and RH20T
• Repository: https://github.com/SpatialVLA/SpatialVLA
• Paper: SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
• Project Page & Videos: https://spatialvla.github.io/

Uses

SpatialVLA relies solely on HuggingFace Transformers 🤗, making deployment extremely easy. If your environment supports transformers >= 4.47.0, you can directly use the following code to load the model and perform inference. (requires 8.5GB of GPU memory).

Direct Use

import torch
from PIL import Image
from transformers import AutoModel, AutoProcessor

model_name_or_path="IPEC-COMMUNITY/spatialvla-4b-224-pt"
processor = AutoProcessor.from_pretrained(model_name_or_path, trust_remote_code=True)

model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16).eval().cuda()

image = Image.open("example.png").conve

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Base Model