README.md

---
license: cc-by-nc-sa-4.0
tags:
- robotics
- vision-language-action-model
- vision-language-model
pipeline_tag: robotics
library_name: transformers
---

# Model Card for InternVLA-M1_spatial

InternVLA-M1 is an open-source, end-to-end vision–language–action (VLA) framework for building and researching generalist robot policies, as presented in the paper [InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy](https://huggingface.co/papers/2510.13778).

- 🌐 Homepage: [InternVLA-M1 Project Page](https://internrobotics.github.io/internvla-m1.github.io/)
- 💻 Codebase: [InternVLA-M1 GitHub Repo](https://github.com/InternRobotics/InternVLA-M1)

<div align="center">
  <img src="https://raw.githubusercontent.com/InternRobotics/InternVLA-M1/main/assets/teaser.png" width="100%" height="100%"/>
</div>

## 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.

## 🔥 Key Features
1. **Modular & Extensible**  
   All core components (model architecture, training data, training strategies, evaluation pipeline) are fully decoupled, enabling independent development, debugging, and extension of each module.

2. **Dual-System and Dual-Supervision**
   InternVLA-M1 integrates both a language head and an action head under a unified framework, enabling collaborative training with dual supervision. 

3. **Efficient Training & Fast Convergence**
   Learns spatial and visual priors from large-scale multimodal pretraining and transfers them via spatial prompt fine-tuning. Achieves strong performance (e.g., SOTA-level convergence on in ~2.5 epochs without separate action pretraining). 

## 🎯 Target Audience
1. Users who want to leverage open-source VLMs (e.g., Qwen2.5-VL) for robot control.
2. Teams co-training action datasets jointly with multimodal (vision–language) data.
3. Researchers exploring alternative VLA architectures and training strategies.

## 📊 Experimental Results
|             | WindowX | Google Robot(VA) | Google Robot(VM) | LIBERO |
|-------------|---------|------------------|------------------|--------|
| $\pi_0$         | 27.1    | 54.8             | 58.8             | 94.2   |
| GR00t       | 61.9    | 44.5             | 35.2             | 93.9   |
| InternVLA-M1 |**71.7** |**76.0**          |**80.7**          |**95.9**|

## 🚀 Quick Start
### 🛠 Environment Setup
```bash
# Clone the repo
git clone https://github.com/InternRobotics/InternVLA-M1

# Create conda environment
conda create -n internvla-m1 python=3.10 -y
conda activate internvla-m1

# Install requirements
pip install -r requirements.txt

# Install FlashAttention2
pip install flash-attn --no-build-isolation

# Install InternVLA-M1
pip install -e .
```

### ⚡ Quick Interactive M1 Demo
Below are two collapsible examples: InternVLA-M1 chat and action prediction.

<details open>
<summary><b>InternVLA-M1 Chat Demo (image Q&A / Spatial Grounding)</b></summary>

```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"
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)
```
</details>

<details>
<summary><b>InternVLA-M1 Action Prediction Demo (two views)</b></summary>

```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"
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))
```
</details>

### 📘 Examples
We provide several end-to-end examples for reference:

* **Reproduce InternVLA-M1 in SimplerEnv**
  [Example](/examples/SimplerEnv)

* **Reproduce InternVLA-M1 in LIBERO**
  [Example](/examples/LIBERO)

* **Training/Deployment on real robots**
  [Example](/examples/real_robot)

## 📈 Model Zoo
We release a series of pretrained models and checkpoints to facilitate reproduction and downstream use.

### ✅ Available Checkpoints
| Model | Description | Link |
|-------|-------------|------|
| **InternVLA-M1** | Main pretrained model | [🤗 Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1) |
| **InternVLA-M1-Pretrain-RT-1-Bridge** | Pretraining on RT-1 Bridge data | [🤗 Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1-Pretrain-RT-1-Bridge) |
| **InternVLA-M1-LIBERO-Long** | Fine-tuned on LIBERO Long-horizon tasks | [🤗 Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1-LIBERO-Long) |
| **InternVLA-M1-LIBERO-Goal** | Fine-tuned on LIBERO Goal-conditioned tasks | [🤗 Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1-LIBERO-Goal) |
| **InternVLA-M1-LIBERO-Spatial** | Fine-tuned on LIBERO Spatial reasoning tasks | [🤗 Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1-LIBERO-Spatial) |
| **InternVLA-M1-LIBERO-Object** | Fine-tuned on LIBERO Object-centric tasks | [🤗 Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1-LIBERO-Object) |

## Training Details
```
action_chunk: 8
batch_size: 128
training_steps: 30k
```

## 🗺️ Roadmap
* [ ] Add Co-Training Multimodel Multitask Readme (now co-training code is already here)
* [x] 0930: Unified Inference Server for Simpler and LIBERO
* [x] 0918: Release model weights

## 🤝 Contributing
We welcome contributions via Pull Requests or Issues.
Please include detailed logs and reproduction steps when reporting bugs.

## 📜 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}
}
```

## 📬 Contact
* Issues: Submit via GitHub Issues with detailed logs and steps

## 🙏 Acknowledgements
We thank the open-source community for their inspiring work. This project builds upon and is inspired by the following projects (alphabetical order):
- [IPEC-COMMUNITY](https://huggingface.co/IPEC-COMMUNITY): Curated OXE / LIBERO style multi-task datasets and formatting examples.
- [Isaac-GR00T](https://github.com/NVIDIA/Isaac-GR00T): Standardized action data loader (GR00T-LeRobot).
- [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL/blob/main/qwen-vl-finetune/README.md): Multimodal input/output format, data loader, and pretrained VLM backbone.
- [CogACT](https://github.com/microsoft/CogACT/tree/main/action_model): Reference for a DiT-style action head design.
- [Llavavla](https://github.com/JinhuiYE/llavavla): Baseline code structure and engineering design references.
- [GenManip Simulation Platform](https://github.com/InternRobotics/GenManip): Simulation platform for generalizable pick-and-place based on Isaac Sim.


Notes:
- If any required attribution or license header is missing, please open an issue and we will correct it promptly.
- All third-party resources remain under their original licenses; users should comply with respective terms.

---

Thanks for using **InternVLA-M1**! 🌟
If you find it useful, please consider giving us a ⭐ on GitHub.