Add model card, pipeline tag, and sample usage

README.md

@@ -1,3 +1,53 @@
 ---
 license: apache-2.0
+pipeline_tag: reinforcement-learning
 ---
+
+# Energy-based Compositional Diffusion Planning (ECD)
+
+This repository contains checkpoints for **ECD (Energy-based Compositional Diffuser)**, presented in the paper [Energy-based Compositional Diffusion Planning](https://huggingface.co/papers/2606.21646) (ICML 2026).
+
+* **Paper**: [arXiv:2606.21646](https://huggingface.co/papers/2606.21646)
+* **Code Repository**: [GitHub - GradientSpaces/ECD](https://github.com/GradientSpaces/ECD)
+
+## Introduction
+
+Compositional diffusion planners aim to solve long-horizon robotic tasks using short training trajectories. ECD (Energy-based Compositional Diffuser) is an inference-only framework that formulates the global trajectory as the minimizer of the sum of local bridge potentials. Instead of stitching local chunk predictions heuristically, ECD defines a single global energy function over all chunks, using its negative gradient to guide the denoising process, ensuring conservative score fields and consistent global modes.
+
+## Usage: Using ECD as a Plug-in
+
+Because ECD operates entirely at inference time, it can wrap any pretrained short-horizon diffusion denoiser. Below is an example of how to instantiate the `CompositionalPolicy` and set the inference type to `ecd_chunk`:
+
+```python
+from ecd.policy import CompositionalPolicy
+
+policy = CompositionalPolicy(
+    diffusion_model=denoiser,                 # Your short-horizon chunk denoiser (see ecd/planner.py)
+    normalizer=normalizer, 
+    ev_n_comp=N,
+    ev_cp_infer_t_type="ecd_chunk",           # Change to "interleave" for the standard CD baseline
+    ecd_config=dict(
+        rank_type="overlap",                  # Map-free candidate ranker
+        base_scale=0.15, 
+        react_scale=0.10,                     # Interior update / boundary-reaction strength
+        markov_type="laplacian", 
+        chunk_react_type="markov",
+    ),
+)
+
+# Generate a long-horizon plan
+plan = policy.plan(start_xy, goal_xy, b_s=40) 
+```
+
+## Citation
+
+```bibtex
+@inproceedings{sun2026ecd,
+  title     = {Energy-based Compositional Diffusion Planning},
+  author    = {Sun, Tao and Mishra, Utkarsh A. and Lu, Jiaxin and Xu, Danfei and Armeni, Iro},
+  booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
+  series    = {PMLR},
+  volume    = {306},
+  year      = {2026}
+}
+```