Improve model card: Add pipeline tag, library, HF paper link, abstract, and usage examples

This PR significantly enhances the model card for InternVLA-M1_spatial by:
- Adding `pipeline_tag: robotics` to improve discoverability on the Hugging Face Hub.
- Specifying `library_name: transformers`, as the model's codebase is built upon both Transformers and Diffusers, with its VLM component (Qwen2.5-VL) being a Transformers model.
- Integrating the official Hugging Face paper link: [InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy](https://huggingface.co/papers/2510.13778).
- Including the paper's abstract for a quick overview of the model's capabilities and methodology.
- Adding a comprehensive "Quick Interactive M1 Demo" section with Python code snippets for both chat/spatial grounding and action prediction, directly from the GitHub repository, enabling users to quickly get started.
- Incorporating additional detailed sections from the GitHub README such as Key Features, Target Audience, Experimental Results, Model Zoo, Roadmap, Contributing, Contact, and Acknowledgements, providing a more complete resource.
- Updating the citation with a more complete BibTeX entry.

These updates aim to make the model card more informative, user-friendly, and discoverable.

Please review and merge this PR.

README.md

@@ -4,12 +4,164 @@ 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.
+
+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
@@ -17,12 +169,45 @@ batch_size: 128
 training_steps: 30k
 ```
 
-## Citation
-```
-@misc{internvla2024,
-  title  = {InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy},
-  author = {InternVLA-M1 Contributors},
-  year   = {2025},
-  booktitle={arXiv},
+## 🗺️ 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.