Genesis: Embodiment Co-Design via Efficient Message and Reward Delivery

This is the codebase accompanying the NeurIPS submission, Genesis: Embodiment Co-Design via Efficient Message and Reward Delivery

Note to reviewers:

1. The codebase will be further cleaned-up in time for camera-ready submission and released on GitHub.
2. This codebase does not reveal any author identity: Our code in this repository is fully anonymized.

Setup

Let's start with python 3.9. It's recommend to create a conda env:

Create new conda env

conda create -n Genesis python=3.9
conda activate Genesis

Install for MuJoCo Simulator and mujoco-py

Install mujoco-py following the instruction here.

Set the following environment variable to avoid problems with multiprocess trajectory sampling:

export OMP_NUM_THREADS=1

(Optional) For MacOS user, you can follow the README_FOR_MAC.md to install MuJoCo on M1/M2/M3 Mac, which is helpful for embodied agents visualization.

Install

pip install -r requirements.txt

Note you may have to follow https://pytorch.org/ setup instructions for installation on your own machine.

Training and Visualization

Training

cd Genesis
chmod 777 scripts/Run_Genesis.sh
./scripts/Run_Genesis.sh

Use the scripts scripts/Run_Genesis.sh, which contain preconfigured arguments and hyperparameters for all the experiments in the paper. Experiments use Hydra to manage configuration.

Visualizing results requires wandb: configure project name with the project key in Genesis/design_opt/conf/config.yaml.

As an example of how to run, this runs Genesis on the crawler environment:

EXPT="crawler"
OMP_NUM_THREADS=1 python -m design_opt.train -m cfg=$EXPT group=$EXPT

You can replace crawler with {crawler, terraincrosser, cheetah, swimmer, glider-regular, glider-medium, glider-hard, walker-regular, walker-medium, walker-hard} to train other environments.

• OMP_NUM_THREADS=1 is essential to prevent CPU ops from hanging.

• The environment is selected with the cfg= flag, each of which corresponds to a YAML file in Genesis/design_opt/cfg. See that folder for the list of available experiments.

• Other hyperparameters are explained in Genesis/Genesis/design_opt/conf/config.yaml and our paper.

Pretrained Models

We also provide pretrained models in Genesis/pretrained_models for visualization.

• You can download pretrained models from Google Drive

• Once the pretrained_models.zip file is downloaded, unzip it under the folder Genesis of this repo:

  unzip pretrained_models.zip
  

After you unzip the file, an example directory hierarchy is:

assets/
design_opt/
...
pretrained_models/
  |-- cheetah/
  |-- crawler/
  |-- ...
  |- walker-regular/
...
scripts/

Visualization

If you have a GUI display, you can run the following command to visualize the pretrained model:

python design_opt/eval.py --train_dir <path_of_model_folder>

For example, you can use pretrained_models to visualize the co-design embodied agent on cheetah generated by Genesis:

python design_opt/eval.py --train_dir /Path/to/Genesis/pretrained_models/cheetah

You can press S to slow the agent, and F to speed up.

The agent is moving quite fast, even for realtime! You can slow it down by pressing S on your keyboard for detailed observation.

Questions?

For any questions, please reach out to authors in the rebuttal phase!