Physically Consistent Preferential Bayesian Optimization

Description

This repository is an implementation of our work, "Physically Consistent Preferential Bayesian Optimization for Food Arrangement". It contains the deep-fried shrimp arrangement task used in our simulation experiments.

Requirements

• Ubuntu 18.04 or 20.04
• Nvidia driver > 470
• Docker

Installation

1. Download and defrost IsaacGym and place it in ./isaacgym.
2. Download and defrost CoppeliaSim and place it in ./simulators/assets.
3. Setup with Docker

$ sudo docker build -t pcpbo .

Usage

1. (Optional) Generate X virtual framebuffer for headless

• Install Xvfb

$ sudo apt-get update && sudo apt-get install xvfb

• Run Xvfb

$ Xvfb :2 -screen 0 1024x768x24 &
$ export DISPLAY=:2

2. Run docker

$ docker run --rm -it --gpus all -e DISPLAY=$DISPLAY\
             -e LOCAL_UID=$(id -u $USER) -e LOCAL_GID=$(id -g $USER)\ 
             -v /tmp/.X11-unix:/tmp/.X11-unix -v $PWD/:/opt/project\
              pcpbo

Estimate a preferred arrangement with Physically Consistent Preferential Bayesian Optimization (PCPBO)

(docker)$ python run_pcpbo.py --task deep-fried_shrimp --pref_weight 0.2 0.8

Generate an arrangement corresponding to a specific weight using Cross Entropy Method (CEM)

(docker)$ python run_cem.py --task deep-fried_shrimp --pref_weight 0.2 0.8

Acknowledgements

The implementation of PBBO is based on this repository .