Add model card for Xiaomi-Robotics-0 (#1)

- Add model card for Xiaomi-Robotics-0 (69e20ebffab476bc1f4e66c03254e04f4f3e5a85)
- Resolve merge conflict with main branch (8e20fd71ae95424fc93cfe5c517b9f83a8050e00)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,7 +1,100 @@
 ---
 license: apache-2.0
+library_name: transformers
 pipeline_tag: robotics
 tags:
 - vision-language-action
 - vla
----
+- robotics
+---
+
+# Xiaomi-Robotics-0
+
+Xiaomi-Robotics-0 is an advanced vision-language-action (VLA) model with 4.7B parameters, specifically engineered for high-performance robotic reasoning and seamless real-time execution. It is pre-trained on large-scale cross-embodiment robot trajectories and vision-language data, enabling generalizable action generation across complex robotic tasks.
+
+- **Paper:** [Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution](https://huggingface.co/papers/2602.12684)
+- **Project Page:** [https://xiaomi-robotics-0.github.io/](https://xiaomi-robotics-0.github.io/)
+- **Repository:** [https://github.com/XiaomiRobotics/Xiaomi-Robotics-0](https://github.com/XiaomiRobotics/Xiaomi-Robotics-0)
+
+## Key Features
+
+* **Strong Generalization**: Pre-trained on diverse cross-embodiment trajectories to handle complex, unseen tasks.
+* **Real-Time Ready**: Optimized with asynchronous execution to minimize inference latency during real-robot rollouts.
+* **Flexible Deployment**: Fully compatible with the Hugging Face `transformers` ecosystem and optimized for consumer-grade GPUs.
+
+## Sample Usage
+
+The model can be loaded and run using the `transformers` library. As the model uses custom code, `trust_remote_code=True` is required.
+
+```python
+import torch
+from transformers import AutoModel, AutoProcessor
+
+# 1. Load model and processor 
+model_path = "XiaomiRobotics/Xiaomi-Robotics-0"
+model = AutoModel.from_pretrained(
+    model_path, 
+    trust_remote_code=True, 
+    attn_implementation="flash_attention_2", 
+    dtype=torch.bfloat16
+).cuda().eval()
+processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True, use_fast=False)
+
+
+# 2. Construct the prompt with multi-view inputs
+language_instruction = "Pick up the red block."
+instruction = (
+    f"<|im_start|>user
+The following observations are captured from multiple views.
+"
+    f"# Base View
+<|vision_start|><|image_pad|><|vision_end|>
+"
+    f"# Left-Wrist View
+<|vision_start|><|image_pad|><|vision_end|>
+"
+    f"Generate robot actions for the task:
+{language_instruction} /no_cot<|im_end|>
+"
+    f"<|im_start|>assistant
+<cot></cot><|im_end|>
+"
+)
+
+# 3. Prepare inputs
+# Assuming `image_base`, `image_wrist` (PIL images), and `proprio_state` (numpy array) are already loaded
+inputs = processor(
+    text=[instruction],
+    images=[image_base, image_wrist], # [PIL.Image, PIL.Image]
+    videos=None,
+    padding=True,
+    return_tensors="pt",
+).to(model.device)
+
+# Add proprioceptive state and action mask
+robot_type = "libero" # Choose based on dataset/robot type
+inputs["state"] = torch.from_numpy(proprio_state).to(model.device, model.dtype).view(1, 1, -1)
+inputs["action_mask"] = processor.get_action_mask(robot_type).to(model.device, model.dtype)
+
+# 4. Generate action 
+with torch.no_grad():
+    outputs = model(**inputs)
+    
+# Decode raw outputs into actionable control commands
+action_chunk = processor.decode_action(outputs.actions, robot_type=robot_type)
+print(f"Generated Action Chunk Shape: {action_chunk.shape}")
+```
+
+## Citation
+
+If you find this project useful, please consider citing:
+
+```bibtex
+@misc{robotics2026xiaomi,
+  title        = {Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution},
+  author       = {Xiaomi Robotics},
+  howpublished={\url{https://xiaomi-robotics-0.github.io}},
+  year         = {2026},
+  note={Project Website}
+}
+```