Paper: "Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search", submitted to the International Conference on Robotics and Automation (ICRA) 2018.
Authors: Rémi Pautrat, Konstantinos Chatzilygeroudis, and Jean-Baptiste Mouret.
git clone --recursive https://github.com/resibots/pautrat_2018_mlei.git
- Boost: C++ Template Libraries
- Eigen: Linear Algebra C++ Library
- realpath:
sudo apt-get install realpath
Important: Make sure you have installed all the dependencies of each repo. Otherwise the build will fail.
From the root of this repo run:
sh compile_all.sh
Note: You have to set the two environment variables RESIBOTS_DIR and LD_LIBRARY_PATH before each use of this code, so you may want to add the two export commands to your .bashrc file:
export RESIBOTS_DIR=[PATH_OF_YOUR_ROOT_DIRECTORY]/install
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:[PATH_OF_YOUR_ROOT_DIRECTORY]/install/dart_path/lib
cd sferes2/build/exp/map_elites_hexapod/
./hexa_duty
The best results were obtained with a 6D descriptor, the body orientation, but other descriptors are available in hexapod_simu/hexapod_dart/include/hexapod_dart/descriptors.hpp.
cd limbo/build/exp/bo_mlei
./bo_mlei_flat_ground [directory_containing_your_priors] [options]
bo_mlei_flat_ground launches the simulation on flat ground, but you can use the other environments as well with bo_mlei_easy_stairs, bo_mlei_medium_stairs and bo_mlei_hard_stairs. Use bo_mlei_robot to use it on a real robot.
Several options are available:
-l: id of the leg you want to remove (-1: no broken leg, 0 and 5: left and right rear legs, 1 and 4: left and right middle legs, 2 and 3: left and right front legs) (default: -1).-s: method used of prior selection (0: random, 1: constant prior, 2: MLEI, default: MLEI).-t: number of types of different priors that you use (default: 4, e.g. for flat ground, easy stairs, medium stairs and hard stairs priors).-p: number of priors used for each type of prior (default: 15).-n: number of iterations for Bayesian optimization (default: 10).-c: controller of the robot given by its 54 parameters (only for replaying one gait, there is no learning here).
From the root of this repo run:
sh clear_all.sh