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README.md

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+---
+library_name: custom
+tags:
+- robotics
+- diffusion
+- mixture-of-experts
+- multi-modal
+license: mit
+datasets:
+- CALVIN
+languages:
+- en
+pipeline_tag: robotics
+---
+# MoDE (Mixture of Denoising Experts) Diffusion Policy
+
+## Model Description
+
+<div style="text-align: center">
+    <img src="MoDE_Figure_1.png" width="800px"/>
+</div>
+
+- [Github Link](https://github.com/intuitive-robots/MoDE_Diffusion_Policy) 
+- [Project Page](https://mbreuss.github.io/MoDE_Diffusion_Policy/) 
+
+This model implements a Mixture of Diffusion Experts architecture for robotic manipulation, combining transformer-based backbone with noise-only expert routing. For faster inference, we can precache the chosen expert for each timestep to reduce computation time.
+
+The model has been pretrained on a subset of OXE for 300k steps and finetuned for downstream tasks on the CALVIN/LIBERO dataset.
+
+## Model Details
+
+### Architecture
+- **Base Architecture**: MoDE with custom Mixture of Experts Transformer
+- **Vision Encoder**: ResNet-50 with FiLM conditioning finetuned from ImageNet
+- **EMA**: Enabled
+- **Action Window Size**: 10
+- **Sampling Steps**: 5 (optimal for performance)
+- **Sampler Type**: DDIM
+
+### Input/Output Specifications
+
+#### Inputs
+- RGB Static Camera: `(B, T, 3, H, W)` tensor
+- RGB Gripper Camera: `(B, T, 3, H, W)` tensor
+- Language Instructions: Text strings
+
+#### Outputs
+- Action Space: `(B, T, 7)` tensor representing delta EEF actions
+
+## Usage
+
+```python
+obs = {
+    "rgb_obs": {
+        "rgb_static": static_image,
+        "rgb_gripper": gripper_image
+    }
+}
+goal = {"lang_text": "pick up the blue cube"}
+action = model.step(obs, goal)
+```
+
+## Training Details
+
+### Configuration
+- **Optimizer**: AdamW
+- **Learning Rate**: 0.0001
+- **Weight Decay**: 0.05
+
+## License
+This model is released under the MIT license.

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