N-LIMB: Neural Limb Optimization for Efficient Morphological Design

This repository contains code for the paper N-LIMB: Neural Limb Optimization for Efficient Morphological Design, currently under review at CoRL 2022.

It provides training code, pretrained models, and scripts to visualize and evaluate training runs.

Installation

Our code relies on Docker and the docker wrapper cpk to manage its dependencies.

To use this codebase, follow these installation steps:

1. Intstall Docker.
2. Install cpk: python -m pip install cpk==0.5.1
3. Clone this repository.
4. From the top level directory, run cpk build to build the docker container.
5. You can enter the container by running cpk run -M -f -X -- --runtime=nvidia.

Additionally, this codebase uses Weights and Biases for logging and visualization, which is free to use for academics. Running a training job will prompt you to login, create an account, or not visualize the results. If you wish to avoid manual logins, place your wandb .netrc file in the assets/wandb_info directory.

Creating a wandb account is only required if you want to track and visualize training runs.

Visualizing Pretrained Models

This repository contains three pretrained models, one for each terrain used in our experiments. They are provided in the exps directory.

• To visualize a trained model, run: cpk run -M -f -X -L viz ++ exps/nlimb_walls --num_envs 1 -- --runtime=nvidia
• To record a video of a trained model, run: cpk run -M -f -X -L record ++ exps/nlimb_walls -- --runtime=nvidia

Replace exps/nlimb_walls with another log directory as needed.

Training from scratch

This repository also provides the complete training code used in our experiments.

To launch a new experiment, run: cpk run -M -f -L train ++ configs/nlimb.gin -- --runtime=nvidia

This codebase uses Gin Config for configuration. The provided config configs/nlimb.gin trains an agent on the random walls terrain using the same hyperparameters as the pretrained model.