---
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
license: mit
pipeline_tag: robotics
tags:
- vision-language-action-model
- vision-language-model
---
# Model Card for InternVLA-M1
## Description:
**InternVLA-M1** is an open-source, end-to-end **vision–language–action (VLA) framework** for building and researching generalist robot policies, as described in the paper [InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy](https://huggingface.co/papers/2510.13778). The checkpoints in this repository were pretrained on the system2 dataset.
- 🌐 Homepage: [InternVLA-M1 Project Page](https://internrobotics.github.io/internvla-m1.github.io/)
- 💻 Codebase: [InternVLA-M1 GitHub Repo](https://github.com/InternRobotics/InternVLA-M1)
## Abstract
We introduce InternVLA-M1, a unified framework for spatial grounding and robot control that advances instruction-following robots toward scalable, general-purpose intelligence. Its core idea is spatially guided vision-language-action training, where spatial grounding serves as the critical link between instructions and robot actions. InternVLA-M1 employs a two-stage pipeline: (i) spatial grounding pre-training on over 2.3M spatial reasoning data to determine ``where to act'' by aligning instructions with visual, embodiment-agnostic positions, and (ii) spatially guided action post-training to decide ``how to act'' by generating embodiment-aware actions through plug-and-play spatial prompting. This spatially guided training recipe yields consistent gains: InternVLA-M1 outperforms its variant without spatial guidance by +14.6% on SimplerEnv Google Robot, +17% on WidowX, and +4.3% on LIBERO Franka, while demonstrating stronger spatial reasoning capability in box, point, and trace prediction. To further scale instruction following, we built a simulation engine to collect 244K generalizable pick-and-place episodes, enabling a 6.2% average improvement across 200 tasks and 3K+ objects. In real-world clustered pick-and-place, InternVLA-M1 improved by 7.3%, and with synthetic co-training, achieved +20.6% on unseen objects and novel configurations. Moreover, in long-horizon reasoning-intensive scenarios, it surpassed existing works by over 10%. These results highlight spatially guided training as a unifying principle for scalable and resilient generalist robots. Code and models are available at this https URL .
## Sample Usage
Below are two examples demonstrating how to use InternVLA-M1 for chat (image Q&A / Spatial Grounding) and action prediction.
InternVLA-M1 Chat Demo (image Q&A / Spatial Grounding)
```python
from InternVLA.model.framework.M1 import InternVLA_M1
from PIL import Image
import requests
from io import BytesIO
import torch
def load_image_from_url(url: str) -> Image.Image:
resp = requests.get(url, timeout=15)
resp.raise_for_status()
img = Image.open(BytesIO(resp.content)).convert("RGB")
return img
saved_model_path = "/PATH/checkpoints/steps_50000_pytorch_model.pt" # Update this path to your downloaded model
internVLA_M1 = InternVLA_M1.from_pretrained(saved_model_path)
# Use the raw image link for direct download
image_url = "https://raw.githubusercontent.com/InternRobotics/InternVLA-M1/InternVLA-M1/assets/table.jpeg"
image = load_image_from_url(image_url)
question = "Give the bounding box for the apple."
response = internVLA_M1.chat_with_M1(image, question)
print(response)
```
InternVLA-M1 Action Prediction Demo (two views)
```python
from InternVLA.model.framework.M1 import InternVLA_M1
from PIL import Image
import requests
from io import BytesIO
import torch
def load_image_from_url(url: str) -> Image.Image:
resp = requests.get(url, timeout=15)
resp.raise_for_status()
img = Image.open(BytesIO(resp.content)).convert("RGB")
return img
saved_model_path = "/PATH/checkpoints/steps_50000_pytorch_model.pt" # Update this path to your downloaded model
internVLA_M1 = InternVLA_M1.from_pretrained(saved_model_path)
image_url = "https://raw.githubusercontent.com/InternRobotics/InternVLA-M1/InternVLA-M1/assets/table.jpeg"
view1 = load_image_from_url(image_url)
view2 = view1.copy()
# Construct input: batch size = 1, two views
batch_images = [[view1, view2]] # List[List[PIL.Image]]
instructions = ["Pick up the apple and place it on the plate."]
if torch.cuda.is_available():
internVLA_M1 = internVLA_M1.to("cuda")
pred = internVLA_M1.predict_action(
batch_images=batch_images,
instructions=instructions,
cfg_scale=1.5,
use_ddim=True,
num_ddim_steps=10,
)
normalized_actions = pred["normalized_actions"] # [B, T, action_dim]
print(normalized_actions.shape, type(normalized_actions))
```
## Citation
If you find this useful in your research, please consider citing:
```bibtex
@article{internvlam1,
title = {InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy},
author = {InternVLA-M1 Contributors},
journal = {arXiv preprint arXiv:2510.13778},
year = {2025}
}
```