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[ICLR 2023] Equivariant Descriptor Fields (EDFs)

Official PyTorch implementation of Equivariant Descriptor Fields: SE(3)-Equivariant Energy-Based Models for End-to-End Visual Robotic Manipulation Learning (ICLR 2023 Poster).

The paper can be found at: https://arxiv.org/abs/2206.08321

Project Website: https://sites.google.com/view/edf-robotics

[!TIP] Please also find our new work, Diffusion-EDFs at: https://sites.google.com/view/diffusion-edfs/home

This is a standalone implementation of EDFs without PyBullet simulation environments. To reproduce our experimental results in the paper, please check the following branch: https://github.com/tomato1mule/edf/tree/iclr2023_rebuttal_ver

EDF+ROS MoveIt example (unstable): https://github.com/tomato1mule/edf_pybullet_ros_experiment

Installation

Step 1. Clone Github repository.

git clone https://github.com/tomato1mule/edf

Step 2. Setup Conda environment.

conda create -n edf python=3.8
conda activate edf

Step 3. Install Dependencies

CUDA=cu113
pip install torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/${CUDA}
pip install torch-cluster==1.6.0 -f https://data.pyg.org/whl/torch-1.11.0+${CUDA}.html
pip install torch-scatter==2.0.9 -f https://data.pyg.org/whl/torch-1.11.0+${CUDA}.html
pip install iopath fvcore
pip install --no-index --no-cache-dir pytorch3d==0.7.2 -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py38_${CUDA}_pyt1110/download.html
pip install -e .

Usage

Train

python pick_train.py
python place_train.py

If you want to load already trained checkpoints, please rename 'checkpoint_example' folder to 'checkpoint'.

Evaluate

Please run the example notebook codes for visualizing sampled poses from trained models (evaluate_pick.ipynb and evaluate_place.ipynb)

View train log

python train_log_viewer.py --logdir="checkpoint/mug_10_demo/ {pick or place} /trainlog_iter_{iter}.gzip"

Citing

If you find our paper useful, please consider citing our paper:

@inproceedings{
ryu2023equivariant,
title={Equivariant Descriptor Fields: SE(3)-Equivariant Energy-Based Models for End-to-End Visual Robotic Manipulation Learning},
author={Hyunwoo Ryu and Hong-in Lee and Jeong-Hoon Lee and Jongeun Choi},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=dnjZSPGmY5O}
}