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 (ICML 2026).
- Paper: arXiv:2606.21646
- Code Repository: GitHub - 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:
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
@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}
}