SceneCollisionNet

This repo contains the code for "Object Rearrangement Using Learned Implicit Collision Functions", an ICRA 2021 paper. For more information, please visit the project website.

License

This repo is released under NVIDIA source code license. For business inquiries, please contact [email protected]. For press and other inquiries, please contact Hector Marinez at [email protected]

Install and Setup

Clone and install the repo (we recommend a virtual environment, especially if training or benchmarking, to avoid dependency conflicts):

git clone --recursive https://github.com/mjd3/SceneCollisionNet.git
cd SceneCollisionNet
pip install -e .

These commands install the minimum dependencies needed for generating a mesh dataset and then training/benchmarking using Docker. If you instead wish to train or benchmark without using Docker, please first install an appropriate version of PyTorch and corresponding version of PyTorch Scatter for your system. Then, execute these commands:

git clone --recursive https://github.com/mjd3/SceneCollisionNet.git
cd SceneCollisionNet
pip install -e .[train]

If benchmarking, replace train in the last command with bench.

To rollout the object rearrangement MPPI policy in a simulated tabletop environment, first download Isaac Gym and place it in the extern folder within this repo. Next, follow the previous installation instructions for training, but replace the train option with policy.

To download the pretrained weights for benchmarking or policy rollout, run bash scripts/download_weights.sh.

Generating a Mesh Dataset

To save time during training/benchmarking, meshes are preprocessed and mesh stable poses are calculated offline. SceneCollisionNet was trained using the ACRONYM dataset. To use this dataset for training or benchmarking, download the ShapeNetSem meshes here (note: you must first register for an account) and the ACRONYM grasps here. Next, build Manifold (an external library included as a submodule):

./scripts/install_manifold.sh

Then, use the following script to generate a preprocessed version of the ACRONYM dataset:

python tools/generate_acronym_dataset.py /path/to/shapenetsem/meshes /path/to/acronym datasets/shapenet

If you have your own set of meshes, run:

python tools/generate_mesh_dataset.py /path/to/meshes datasets/your_dataset_name

Note that this dataset will not include grasp data, which is not needed for training or benchmarking SceneCollisionNet, but is be used for rolling out the MPPI policy.

Training/Benchmarking with Docker

First, install Docker and nvidia-docker2 following the instructions here. Pull the SceneCollisionNet docker image from DockerHub (tag scenecollisionnet) or build locally using the provided Dockerfile (docker build -t scenecollisionnet .). Then, use the appropriate configuration .yaml file in cfg to set training or benchmarking parameters (note that cfg file paths are relative to the Docker container, not the local machine) and run one of the commands below (replacing paths with your local paths as needed; -v requires absolute paths).

Train a SceneCollisionNet

Edit cfg/train_scenecollisionnet.yaml, then run:

docker run --gpus all --rm -it -v /path/to/dataset:/dataset:ro -v /path/to/models:/models:rw -v /path/to/cfg:/cfg:ro scenecollisionnet /SceneCollisionNet/scripts/train_scenecollisionnet_docker.sh

Train a RobotCollisionNet

Edit cfg/train_robotcollisionnet.yaml, then run:

docker run --gpus all --rm -it -v /path/to/models:/models:rw -v /path/to/cfg:/cfg:ro scenecollisionnet /SceneCollisionNet/scripts/train_robotcollisionnet_docker.sh

Benchmark a SceneCollisionNet

Edit cfg/benchmark_scenecollisionnet.yaml, then run:

docker run --gpus all --rm -it -v /path/to/dataset:/dataset:ro -v /path/to/models:/models:ro -v /path/to/cfg:/cfg:ro -v /path/to/benchmark_results:/benchmark:rw scenecollisionnet /SceneCollisionNet/scripts/benchmark_scenecollisionnet_docker.sh

Benchmark a RobotCollisionNet

Edit cfg/benchmark_robotcollisionnet.yaml, then run:

docker run --gpus all --rm -it -v /path/to/models:/models:rw -v /path/to/cfg:/cfg:ro -v /path/to/benchmark_results:/benchmark:rw scenecollisionnet /SceneCollisionNet/scripts/train_robotcollisionnet_docker.sh

Loss Plots

To get loss plots while training, run:

docker exec -d <container_name> python3 tools/loss_plots.py /models/<model_name>/log.csv

Benchmark FCL or SDF Baselines

Edit cfg/benchmark_baseline.yaml, then run:

docker run --gpus all --rm -it -v /path/to/dataset:/dataset:ro -v /path/to/benchmark_results:/benchmark:rw -v /path/to/cfg:/cfg:ro scenecollisionnet /SceneCollisionNet/scripts/benchmark_baseline_docker.sh

Training/Benchmarking without Docker

First, install system dependencies. The system dependencies listed assume an Ubuntu 18.04 install with NVIDIA drivers >= 450.80.02 and CUDA 10.2. You can adjust the dependencies accordingly for different driver/CUDA versions. Note that the NVIDIA drivers come packaged with EGL, which is used during training and benchmarking for headless rendering on the GPU.

System Dependencies

See Dockerfile for a full list. For training/benchmarking, you will need:

python3-dev
python3-pip
ninja-build
libcudnn8=8.1.1.33-1+cuda10.2
libcudnn8-dev=8.1.1.33-1+cuda10.2
libsm6
libxext6
libxrender-dev
freeglut3-dev
liboctomap-dev
libfcl-dev
gifsicle
libfreetype6-dev
libpng-dev

Python Dependencies

Follow the instructions above to install the necessary dependencies for your use case (either the train, bench, or policy options).

Train a SceneCollisionNet

Edit cfg/train_scenecollisionnet.yaml, then run:

PYOPENGL_PLATFORM=egl python tools/train_scenecollisionnet.py

Train a RobotCollisionNet

Edit cfg/train_robotcollisionnet.yaml, then run:

python tools/train_robotcollisionnet.py

Benchmark a SceneCollisionNet

Edit cfg/benchmark_scenecollisionnet.yaml, then run:

PYOPENGL_PLATFORM=egl python tools/benchmark_scenecollisionnet.py

Benchmark a RobotCollisionNet

Edit cfg/benchmark_robotcollisionnet.yaml, then run:

python tools/benchmark_robotcollisionnet.py

Benchmark FCL or SDF Baselines

Edit cfg/benchmark_baseline.yaml, then run:

PYOPENGL_PLATFORM=egl python tools/benchmark_baseline.py

Policy Rollout

To view a rearrangement MPPI policy rollout in a simulated Isaac Gym tabletop environment, run the following command (note that this requires a local machine with an available GPU and display):

python tools/rollout_policy.py --self-coll-nn weights/self_coll_nn --scene-coll-nn weights/scene_coll_nn --control-frequency 1

There are many possible options for this command that can be viewed using the --help command line argument and set with the appropriate argument. If you get RuntimeError: CUDA out of memory, try reducing the horizon (--mppi-horizon, default 40), number of trajectories (--mppi-num-rollouts, default 200) or collision steps (--mppi-collision-steps, default 10). Note that this may affect policy performance.

Citation

If you use this code in your own research, please consider citing:

@inproceedings{danielczuk2021object,
  title={Object Rearrangement Using Learned Implicit Collision Functions},
  author={Danielczuk, Michael and Mousavian, Arsalan and Eppner, Clemens and Fox, Dieter},
  booktitle={Proc. IEEE Int. Conf. Robotics and Automation (ICRA)},
  year={2021}
}