Improve model card: Add pipeline tag, paper link, abstract, and sample usage

This PR enhances the model card for InternVLA-M1 by:

- Updating the `license` to `mit` based on the explicit badge in the GitHub repository.
- Adding the `pipeline_tag: robotics` to the metadata, ensuring the model appears in the [robotics pipeline filter](https://huggingface.co/models?pipeline_tag=robotics) on the Hugging Face Hub.
- Including a direct link to the official Hugging Face paper page, [InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy](https://huggingface.co/papers/2510.13778), in the model description.
- Adding the paper's abstract as a dedicated section for a quick overview.
- Including two detailed Python code snippets for "InternVLA-M1 Chat Demo (image Q&A / Spatial Grounding)" and "InternVLA-M1 Action Prediction Demo (two views)", extracted directly from the GitHub repository's `Quick Interactive M1 Demo` section, to help users easily get started with the model.
- Updating the `Citation` section with the more complete BibTeX entry from the GitHub README.

Please review and merge this PR.

README.md

@@ -1,31 +1,109 @@
 ---
-license: cc-by-nc-sa-4.0
 base_model:
 - Qwen/Qwen2.5-VL-3B-Instruct
+library_name: transformers
+license: mit
+pipeline_tag: robotics
 tags:
-- robotics
 - vision-language-action-model
 - vision-language-model
-library_name: transformers
 ---
+
 # 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. The checkpoints in this repository were pretrained on the system2 dataset.
+**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)
 
 
 ![image/png](https://github.com/InternRobotics/InternVLA-M1/raw/InternVLA-M1/assets/teaser.png)
 
+## 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
 
-## Citation
+Below are two examples demonstrating how to use InternVLA-M1 for chat (image Q&A / Spatial Grounding) 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" # 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)
 ```
-@misc{internvla2024,
-  title  = {InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy},
-  author = {InternVLA-M1 Contributors},
-  year   = {2025},
-  booktitle={arXiv},
+</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" # 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))
+```
+</details>
+
+## 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}
 }
 ```