Neural Fidelity Warping for Efficient Robot Morphology Design

[Video] [Paper] [Project Website]

Sha Hu¹, Zeshi Yang¹, Greg Mori¹

¹Simon Fraser Unversity

This is the official implementation of the paper "Neural Fidelity Warping for Efficient Robot Morphology Design".

Getting started

Please first download and install the anaconda3:

wget https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh
bash ~/Anaconda3-2019.10-Linux-x86_64.sh

Then create a conda virtual environment and install all required packages:

conda env create --file nfwbo.yaml --name nfwbo

Set the environment variables that specify the python path:

export PYTHONPATH="~/FWBO:$PYTHONPATH"

Commands

Method	Env	Command
Random	Cheetah	python nfwbo/main.py --config random_cheetahurdf13lr_N200_T200 --m random
CMA-ES	Cheetah	python nfwbo/main.py --config cmaes_cheetahurdf13lr_N200_T200 --m cmaes
BO	Cheetah	python nfwbo/main.py --config sfbo_es_cheetahurdf13lr_N200_T200 --m sfbo
HPC-BBO	Cheetah	python nfwbo/main.py --config hpcbbo_es_cheetahurdf13lr_N200_T200 --m hpcbbo
BOCA	Cheetah	python nfwbo/main.py --config boca_cmfes_cheetahurdf13lr_N200_T200 --m boca
FABOLAS	Cheetah	python nfwbo/main.py --config fabolas_cmfes_cheetahurdf13lr_N200_T200 --m fabolas
NFW(Ours)	Cheetah	python nfwbo/main.py --config fidelitywarping_cmfes_cheetahurdf13lr_N200_T200 --m fidelitywarping

Method	Env	Command
Random	Ant	python nfwbo/main.py --robot ant --config random_ant25lr_N200_T200 --m random
CMA-ES	Ant	python nfwbo/main.py --robot ant --config cmaes_ant25lr_N200_T200 --m cmaes
BO	Ant	python nfwbo/main.py --robot ant --config sfbo_es_ant25lr_N200_T200 --m sfbo
HPC-BBO	Ant	python nfwbo/main.py --robot ant --config hpcbbo_es_ant25lr_N200_T200 --m hpcbbo
BOCA	Ant	python nfwbo/main.py --robot ant --config boca_cmfes_ant25lr_N200_T200 --m boca
FABOLAS	Ant	python nfwbo/main.py --robot ant --config fabolas_cmfes_ant25lr_N200_T200 --m fabolas
NFW(Ours)	Ant	python nfwbo/main.py --robot ant --config fidelitywarping_cmfes_ant25lr_N200_T200 --m fidelitywarping

Visualize results

With a saved experiment directory, a robot design and a trained policy, a video that shows the robot, topdown-view trajectories and rewards can be generated by

python nfwbo/eval_scripts/evaluate_robot_policy.py --exp_dir EXP_DIR --model_dir EXP_DIR/model
# EXP_DIR is the path of the saved experiments

Also, demos can be found in the video and project website.

Code Structure Overview

nfwbo
  |- configs               # all experiment configs should be placed here
  |    |- ant              # configs for ant env
  |    |- cheetah          # configs for cheetah env
  |    |- default_configs.py       # defines one default config for all methods
  |
  |- design_optimize_alg   # implements core robot morphology optimization algorithms
  |    |- acquisitions     # implements acquisitions functions for both single fidelity BO and multi-fidelity BO  
  |    |- optimizer        # implements optimization algorithms reported in the experiment section
  |        |- BasicOptimizer.py    # implements a plain optimizer 
  |        |- CmaEsOptimizer.py    # implements CMA-ES
  |        |- CmfBayesoptimizer.py # implements BOCA/FABOLAS/NFW(ours)
  |        |- HpcBatchBayesOptimizer.py   # implements HPC-BBO
  |        |- RandomOptimizer.py   # implements Random
  |        |- SfBayesOptimizer.py  # implements BO
  |    |- surrogates               # implements surrogate models for all Beyesian Optimization methods
  |        |- kernels
  |        |- models
  |            |- FabolasModel.py  # implements FABOLAS model 
  |            |- FidelitywarpingModel.py # implements NFW(ours) model
  |            |- siglegpModel.py  # implements single fidelity guassian process model
  |        |- transformations      # implements neural fidelity warping 
  |    |- test_functions           # implements a class for wrapping multi-fidelity objectives
  |    |- DesignOptimize.py        # implements a core class for setting optimiziers, environments, RL and logging
  |
  |- envs                  # all RL environments should be defined here
  |- eval_scripts
       |-evaluate_robot_policy.py  # generates demo videos  
  |- helpers 
  |    |- helpers.py       # general utilities, create experimental path / set random seeds etc
  |- RL                    # all codes related to RL
  |
  |- main.py               # main script to start optimization loop + logging

Citation

If you find this work useful in your research, please consider citing:

@misc{hu2020neural,
      title={Neural fidelity warping for efficient robot morphology design}, 
      author={Sha Hu and Zeshi Yang and Greg Mori},
      year={2020},
      eprint={2012.04195},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}

Acknowledgements

The baseline FABOLAS is implemented based on the project.