SIGGRAPH 2021: Discovering Diverse Athletic Jumping Strategies

project page

paper

demo video

Prerequisites

Important Notes

We suspect there are bugs in linux gcc > 9.2 or kernel > 5.3 or our code somehow is not compatible with that. Our code has large numerical errors from unknown source given the new C++ compiler. Please use older versions of C++ compiler or test the project on Windows.

C++ Setup

This project has C++ components. There is a cmake project inside Kinematic folder. We have setup the CMake project so that it can be built on both linux and Windows. Use cmake, cmake-gui or visual studio to build the project. It requires eigen library.

Python Setup

Install the Python requirements listed in requirements.txt. The version shouldn't matter. You should be safe to install the latest versions of these packages.

Rendering Setup

To visualize training results, please set up our simulation renderer.

• Clone and follow build instructions in UnityKinematics. This is a flexible networking utility that will send raw simulation geometry data to Unity for rendering purpose.
• Copy [UnityKinematics build folder]/pyUnityRenderer to this root project folder.
• Here's a sample Unity project called SimRenderer in which you can render the scenes for this project. Clone SimRenderer outside this project folder.
• After building UnityKinematics, copy [UnityKinematics build folder]/Assets/Scripts/API to SimRenderer/Assets/Scripts. Start Unity, load SimRenderer project and it's ready to use.

Training P-VAE

We have included a pre-trained model in results/vae/models/13dim.pth. If you would like to retrain the model, run the following:

python train_pose_vae.py

This will generate the new model in results/vae/test**/test.pth. Copy the .pth file and the associated .pth.norm.npy file into results/vae/models. Change presets/default/vae/vae.yaml under the model key to use your new model.

Train Run-ups

python train.py runup

Modify presets/custom/runup.yaml to change parts of the target take-off features. Refer to Appendix A in the paper to see reference parameters.

After training, run

python once.py runup no_render results/runup***/checkpoint_2000.tar

to generate take-off state file in npy format used to train take-off controller.

Train Jumpers

Open presets/custom/jump.yaml, change env.highjump.initial_state to the path to the generated take-off state file, like results/runup***/checkpoint_2000.tar.npy. Then change env.highjump.wall_rotation to specify the wall orientation (in degrees). Refer to Appendix A in the paper to see reference parameters (note that we use radians in the paper). Run

python train.py jump

to start training.

Start the provided SimRenderer (in Unity), enter play mode, the run

python evaluate.py jump results/jump***/checkpoint_***.tar

to evaluate the visualize the motion at any time. Note that env.highjump.initial_wall_height must be set to the training height at the time of this checkpoint for correct evaluation. Training height information is available through training logs, available both in the console and through tensorboard logs. You can start tensorboard through

python -m tensorboard.main --bind_all --port xx --logdir results/jump***/