# InternVLA-M1
**Repository Path**: chen-liangwei/InternVLA-M1
## Basic Information
- **Project Name**: InternVLA-M1
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: InternVLA-M1
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-01-22
- **Last Updated**: 2026-01-22
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# InternVLA-M1
**InternVLA-M1** is a spatially guided vision-language-action framework for generalist robot policy.
https://github.com/user-attachments/assets/e83ae046-a503-46a8-95e4-ef381919b7f8
[](https://arxiv.org/abs/2510.13778) [](https://internrobotics.github.io/internvla-m1.github.io) [](https://youtu.be/n129VDqJCk4) [](LICENSE)
## π₯ 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).
## π― 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 (visionβlanguage) data.
3. Researchers exploring alternative VLA architectures and training strategies.
## π 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**|
# π Quick Start
## π 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 .
```
## β‘ Quick Interactive M1 Demo
Below are two collapsible examples: InternVLA-M1 chat 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"
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"
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))
```
## π 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)
## π Model Zoo
We release a series of pretrained models and checkpoints to facilitate reproduction and downstream use.
### β
Available Checkpoints
| Model | Description | Link |
|-------|-------------|------|
| **InternVLA-M1** | Main pretrained model | [π€ Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1) |
| **InternVLA-M1-Pretrain-RT-1-Bridge** | Pretraining on RT-1 Bridge data | [π€ Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1-Pretrain-RT-1-Bridge) |
| **InternVLA-M1-LIBERO-Long** | Fine-tuned on LIBERO Long-horizon tasks | [π€ Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1-LIBERO-Long) |
| **InternVLA-M1-LIBERO-Goal** | Fine-tuned on LIBERO Goal-conditioned tasks | [π€ Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1-LIBERO-Goal) |
| **InternVLA-M1-LIBERO-Spatial** | Fine-tuned on LIBERO Spatial reasoning tasks | [π€ Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1-LIBERO-Spatial) |
| **InternVLA-M1-LIBERO-Object** | Fine-tuned on LIBERO Object-centric tasks | [π€ Hugging Face](https://huggingface.co/InternRobotics/InternVLA-M1-LIBERO-Object) |
# πΊοΈ Roadmap
* [x] 1215: Add Co-Training Multimodel Multitask [README.md](examples/CoTraininig/README.md)
* [x] 0930: Unified Inference Server for Simpler and LIBERO
* [x] 0918: Release model weights
# π€ Contributing
We welcome contributions via Pull Requests or Issues.
Please include detailed logs and reproduction steps when reporting bugs.
# π 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}
}
```
# π¬ Contact
* Issues: Submit via GitHub Issues with detailed logs and steps
# π 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**! π
If you find it useful, please consider giving us a β on GitHub.