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Neural Descriptor Fields (NDF)

PyTorch implementation for training continuous 3D neural fields to represent dense correspondence across objects, and using these descriptor fields to mimic demonstrations of a pick-and-place task on a robotic system

This is the reference implementation for our paper:

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation

PDF | Video

Anthony Simeonov*, Yilun Du*, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal**, Vincent Sitzmann** (*Equal contribution, order determined by coin flip. **Equal advising)

Google Colab

If you want a quickstart demo of NDF without installing anything locally, we have written a Colab. It runs the same demo as the Quickstart Demo section below where a local coordinate frame near one object is sampled, and the corresponding local frame near a new object (with a different shape and pose) is recovered via our energy optimization procedure.

Setup

Clone this repo

git clone --recursive https://github.com/anthonysimeonov/ndf_robot.git
cd ndf_robot

Install dependencies (using a virtual environment is highly recommended):

pip install -e .

Setup additional tools (Franka Panda inverse kinematics -- unnecessary if not using simulated robot for evaluation):

cd pybullet-planning/pybullet_tools/ikfast/franka_panda
python setup.py

Setup environment variables (this script must be sourced in each new terminal where code from this repository is run)

source ndf_env.sh

Quickstart Demo

Download pretrained weights

./scripts/download_demo_weights.sh

Download data assets

./scripts/download_demo_data.sh

Run example script

cd src/ndf_robot/eval
python ndf_demo.py

The code in the NDFAlignmentCheck class in the file src/ndf_robot/eval/ndf_alignment.py contains a minimal implementation of our SE(3)-pose energy optimization procedure. This is what is used in the Quickstart demo above. For a similar implementation that is integrated with our pick-and-place from demonstrations pipeline, see src/ndf_robot/opt/optimizer.py

Training

Download all data assets

If you want the full dataset (~150GB for 3 object classes):

./scripts/download_training_data.sh 

If you want just the mug dataset (~50 GB -- other object class data can be downloaded with the according scripts):

./scripts/download_mug_training_data.sh 

If you want to recreate your own dataset, see Data Generation section

Run training

cd src/ndf_robot/training
python train_vnn_occupancy_net.py --obj_class all --experiment_name  ndf_training_exp

More information on training here

Evaluation with simulated robot

Make sure you have set up the additional inverse kinematics tools (see Setup section)

Download all the object data assets

./scripts/download_obj_data.sh

Download pretrained weights

./scripts/download_demo_weights.sh

Download demonstrations

./scripts/download_demo_demonstrations.sh

Run evaluation

If you are running this command on a remote machine, be sure to remove the --pybullet_viz flag!

cd src/ndf_robot/eval
CUDA_VISIBLE_DEVICES=0 python evaluate_ndf.py \
        --demo_exp grasp_rim_hang_handle_gaussian_precise_w_shelf \
        --object_class mug \
        --opt_iterations 500 \
        --only_test_ids \
        --rand_mesh_scale \
        --model_path multi_category_weights \
        --save_vis_per_model \
        --config eval_mug_gen \
        --exp test_mug_eval \
        --pybullet_viz

More information on experimental evaluation can be found here.

Data Generation

Download all the object data assets

./scripts/download_obj_data.sh

Run data generation

cd src/ndf_robot/data_gen
python shapenet_pcd_gen.py \
    --total_samples 100 \
    --object_class mug \
    --save_dir test_mug \
    --rand_scale \
    --num_workers 2

More information on dataset generation can be found here.

Collect new demonstrations with teleoperated robot in PyBullet

Make sure you have downloaded all the object data assets (see Data Generation section)

Run teleoperation pipeline

cd src/ndf_robot/demonstrations
python label_demos.py --exp test_bottle --object_class bottle --with_shelf

More information on collecting robot demonstrations can be found here.

Citing

If you find our paper or this code useful in your work, please cite our paper:

@article{simeonovdu2021ndf,
  title={Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation},
  author={Simeonov, Anthony and Du, Yilun and Tagliasacchi, Andrea and Tenenbaum, Joshua B. and Rodriguez, Alberto and Agrawal, Pulkit and Sitzmann, Vincent},
  journal={arXiv preprint arXiv:2112.05124},
  year={2021}
}

Acknowledgements

Parts of this code were built upon the implementations found in the occupancy networks repo and the vector neurons repo. Check out their projects as well!