InternVLA-M1

InternVLA-M1 is a Open-source Spatially Grounded Foundation Model for Generalist Robot Policy

demo_mini.mp4

🔥 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

# 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.

InternVLA-M1 Chat Demo (image Q&A / Spatial Grounding)

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)

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))

📘 Examples

We provide several end-to-end examples for reference:

• Reproduce InternVLA-M1 in simplerEnv Example

• Training/Deployment on real robots Example

• Extending InternVLA-M1 Example

📈 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
InternVLA-M1-Pretrain-RT-1-Bridge	Pretraining on RT-1 Bridge data	🤗 Hugging Face
InternVLA-M1-LIBERO-Long	Fine-tuned on LIBERO Long-horizon tasks	🤗 Hugging Face
InternVLA-M1-LIBERO-Goal	Fine-tuned on LIBERO Goal-conditioned tasks	🤗 Hugging Face
InternVLA-M1-LIBERO-Spatial	Fine-tuned on LIBERO Spatial reasoning tasks	🤗 Hugging Face
InternVLA-M1-LIBERO-Object	Fine-tuned on LIBERO Object-centric tasks	🤗 Hugging Face

🗺️ Roadmap

• [✅] Release model weights
• Add multi-task mixed training examples
• Unify evaluation scripts and metrics

🤝 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:

@misc{internvla2024,
  title  = {InternVLA-M1: Latent Spatial Grounding for Instruction-Following Robotic Manipulation},
  author = {InternVLA-M1 Contributors},
  year   = {2025},
  booktitle={arXiv},
}

📬 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: Curated OXE / LIBERO style multi-task datasets and formatting examples.
• Isaac-GR00T: Standardized action data loader (GR00T-LeRobot).
• Qwen2.5-VL: Multimodal input/output format, data loader, and pretrained VLM backbone.
• CogACT: Reference for a DiT-style action head design.
• llavavla: Baseline code structure and engineering design references.
• GenManip Simulation Platform: 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.

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