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EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning

Jingyun Yang*, Zi-ang Cao*, Congyue Deng, Rika Antonova, Shuran Song, Jeannette Bohg

YouTube

Overview figure

This repository includes:

  • Implementation of the EquiBot method and a Diffusion Policy baseline that takes point clouds as input.
  • A set of three simulated mobile manipulation environments: Cloth Folding, Object Covering, and Box Closing.
  • Data generation, training, and evaluation scripts that accompany the above algorithms and environments.

Getting Started

Installation

This codebase is tested with the following setup: Ubuntu 20.04, an RTX 3090 GPU, CUDA 12.4 (updated by Pinxin based on the XHT computer). In the root directory of the repository, run the following commands:

conda create -n equibot python=3.10 -y
conda activate equibot

pip install fvcore iopath ffmpeg
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu124
pip install "git+https://github.com/facebookresearch/pytorch3d.git"

pip install -e .
pip uninstall numpy
pip install numpy==1.26.4

Then, in the last two lines of this config file, enter the wandb entity and project names for logging purposes. If you do not have a wandb account yet, you can register here.

Demonstration Generation

The following code generates demonstrations for simulated mobile environments. To change the number of generated demos, change --num_demos 50 to a different number.

python -m equibot.envs.sim_mobile.generate_demos --data_out_dir ./data/fold \
    --num_demos 50 --cam_dist 2 --cam_pitches -75 --task_name fold

python -m equibot.envs.sim_mobile.generate_demos --data_out_dir ./data/cover \
    --num_demos 50 --cam_dist 2 --cam_pitches -75 --task_name cover

python -m equibot.envs.sim_mobile.generate_demos --data_out_dir ./data/close \
    --num_demos 50 --cam_dist 1.5 --cam_pitches -45 --task_name close

Training

The following code runs training for our method and the Diffusion Policy baseline. Fill the dataset path with the data_out_dir argument in the previous section. Make sure the dataset path ends with pcs. To run this code for the cover and close environments, substitute occurrences of fold with cover or close.

# diffusion policy baseline (takes point clouds as input)
python -m equibot.policies.train --config-name fold_mobile_dp \
    prefix=sim_mobile_fold_7dof_dp \
    data.dataset.path=/home/px/ws/github/equibot/data/fold/pcs

# our method (equibot)
python -m equibot.policies.train --config-name fold_mobile_equibot \
    prefix=sim_mobile_fold_7dof_equibot \
    data.dataset.path=/home/px/ws/github/equibot/data/fold/pcs

Evaluation

The commands below evaluate the trained EquiBot policy on the four different setups mentioned in the paper: Original, R+Su, R+Sn, and R+Sn+P. To run these evaluations for the DP baseline, replace all occurrences of equibot todp. For the log directory, fill [log_dir] with the absolute path to the log directory. By default, this directory is ./log.

# Original setup
python -m equibot.policies.eval --config-name fold_mobile_equibot \
    prefix="eval_original_sim_mobile_fold_equibot" mode=eval \
    training.ckpt="/home/px/ws/github/equibot/logs/train/sim_mobile_fold_7dof_equibot/ckpt01999.pth" \
    env.args.max_episode_length=50 env.vectorize=true

# R+Su setup
python -m equibot.policies.eval --config-name fold_mobile_equibot \
    prefix="eval_rsu_sim_mobile_fold_7dof_equibot" mode=eval \
    training.ckpt="[log_dir]/train/sim_mobile_fold_7dof_equibot/ckpt01999.pth" \
    env.args.scale_high=2 env.args.uniform_scaling=true \
    env.args.randomize_rotation=true env.args.randomize_scale=true env.vectorize=true

# R+Sn setup
python -m equibot.policies.eval --config-name fold_mobile_equibot \
    prefix="eval_rsn_sim_mobile_fold_7dof_equibot" mode=eval \
    training.ckpt="[log_dir]/train/sim_mobile_fold_7dof_equibot/ckpt01999.pth" \
    env.args.scale_high=2 env.args.scale_aspect_limit=1.33 \
    env.args.randomize_rotation=true env.args.randomize_scale=true env.vectorize=true

# R+Sn+P setup
python -m equibot.policies.eval --config-name fold_mobile_equibot \
    prefix="eval_rsnp_sim_mobile_fold_7dof_equibot" mode=eval \
    training.ckpt="[log_dir]/train/sim_mobile_fold_7dof_equibot/ckpt01999.pth" \
    env.args.scale_high=2 env.args.scale_aspect_limit=1.33 \
    env.args.randomize_rotation=true env.args.randomize_scale=true \
    +env.args.randomize_position=true +env.args.rand_pos_scale=0.5 env.vectorize=true

Tips

  • To kill wandb after Ctrl+c in the terminal:
ps aux | grep wandb | grep -v grep | awk '{print $2}' | xargs kill -9

License

This codebase is licensed under the terms of the MIT License.