This project is an implementation of the paper Learning-from-Incremental-Directional-Corrections, co-authored by Wanxin Jin, Todd D. Murphey, and Shaoshuai Mou. Please find more details in
- Paper: https://arxiv.org/abs/2011.15014 for technical details.
- Blog: https://wanxinjin.github.io/posts/lfdc for a brief problem introduction.
This repo has been tested with:
- Ubuntu 20.04.2 LTS, Python 3.8.5, CasADi 3.5.5, Numpy 1.20.1, Cvxpy 1.1.11, Mosek 9.2.40, Pynput 1.7.3.
- macOS 11.2.3, Python 3.9.2, CasADi 3.5.5, Numpy 1.20.1, Cvxpy 1.1.11, Mosek 9.2.40, IPOPT 3.13.4, Pynput 1.7.3.
The current version of the project consists of following folders or files:
- LFC: a package including an optimal control solver and a maximum volume inscribed ellipsoid (MVE) solver.
- JinEvn: an independent package providing various robot environments to simulate on.
- Simulations: a directory including different simulation examples used in the paper.
- lib: a library folder including many helper classes.
- examples: a directory including examples about robot arm and quadrotor.
- test: a directory including test files for Mosek, and CasADi with IPOPT.
Please make sure that the following packages have already been installed before you use the codes.
- CasADi: version >= 3.5.1.
- Numpy: version >= 1.18.1.
- Pynput: version >= 1.7.1, used for keyboard interface when run the human-robot games.
- IPOPT: need this for solving NLP in CasADi, the binary installation is okay.
- Cvxpy: version >= 1.0.31, used for solving MVE.
- Mosek: version >= 9.2.16, used for solving MVE (core solver). License Required
- Scipy
- Transforms3d
Mosek requires a license to use. You can request an academic license mosek.lic at https://www.mosek.com/products/academic-licenses/. Then place mosek.lic inside a folder mosek (you may need to create it manually) under the user's home directory.
Example: _userid_ is your User ID on the computer.
-
Windows:
c:\users\_userid_\mosek\mosek.lic -
Linux:
/home/_userid_/mosek/mosek.lic -
macOS:
/User/_userid_/mosek/mosek.lic
You can install Mosek by pip (an example shown below). You can run test/test_mosek_solver.py to test if you installed Mosek work properly.
- For Linux:
$ sudo apt update
$ sudo apt install build-essential coinor-libipopt-dev
$ pip3 install casadi numpy scipy transforms3d pynput cvxpy mosek
$ git clone https://github.com/zehuilu/Learning-from-Directional-Corrections.git
$ cd <ROOT_DIRECTORY>
$ mkdir data
$ python3 test/test_mosek_solver.py # test mosek
$ python3 test/test_casadi_ipopt.py # test ipopt
- For macOS:
$ brew update
$ pip3 install casadi numpy scipy transforms3d pynput cvxpy mosek
$ # remember to set up MOSEK license
To make IPOPT work with macOS default compiler Clang, we need GFortran, which comes with GCC. More details see here.
$ brew update
$ brew install gcc ipopt libomp
Sometimes CasADi can't find GFortran, and returns an error Cannot load shared library 'libcasadi_nlpsol_ipopt.so' due to "Library not loaded: @rpath/libgfortran.4.dylib". In this case, you need to manually create a symlink for libgfortran.4.dylib. An example is shown below:
First, search a specific directory to make sure you do have libgfortran.X.dylib (X is your libgfortran's version):
$ grep -l 'libgfortran' /usr/local/Cellar/gcc/<GCC_VERSION>/lib/gcc/<GCC_VERSION_FIRST_SECTION>/**
$ # example: grep -l 'libgfortran' /usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10/**
If you see some files includes libgfortran.X.dylib, for example:
grep: /usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10/gcc: Is a directory
/usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10/libgfortran.5.dylib
/usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10/libgfortran.a
/usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10/libgfortran.dylib
Then continue with the following instructions. If not, try to install GCC first.
Let's say you have libgfortran.5.dylib (or generally libgfortran.X.dylib) but CasADi cannot load libgfortran.4.dylib, then you need to create a symlink in a specific directory /usr/local/lib/, which links to libgfortran.X.dylib. (The backward compatibility should be fine.)
$ ln /usr/local/Cellar/gcc/<GCC_VERSION>/lib/gcc/<GCC_VERSION_FIRST_SECTION>/libgfortran.X.dylib /usr/local/lib/libgfortran.4.dylib
$ # example: ln /usr/local/Cellar/gcc/10.2.0_4/lib/gcc/10/libgfortran.5.dylib /usr/local/lib/libgfortran.4.dylib
Finally, run test/test_casadi_ipopt.py to test if IPOPT works correctly with CasADi.
$ git clone https://github.com/zehuilu/Learning-from-Directional-Corrections.git
$ cd <ROOT_DIRECTORY>
$ mkdir data
$ python3 test/test_mosek_solver.py # test mosek
$ python3 test/test_casadi_ipopt.py # test ipopt
Feel free to start an issue if you have any questions or post your questions/thoughts in our Discussions channel. We're happy to help!
The two-link robot arm game. The goal is to let a human player teach the robot arm to learn a valid cost function by applying incremental directional corrections, such that it successfully moves from the initial condition (current pose) to the target position (upward pose) while avoiding the obstacle. |
The 6-DoF quadrotor game. The goal of this game is to let a human player to teach a 6-DoF quadrotor system to learn a valid control cost function by providing directional corrections, such that it can successfully fly from the initial position (in bottom left), pass through a gate (colored in brown), and finally land on the specified target (in upper right). |
You can directly run examples/run_robotarm_game.py to enter the two-link robot-arm game.
$ cd <ROOT_DIRECTORY>
$ python3 examples/run_robotarm_game.py
The goal of this game is to let a human player teach the robot arm to learn a valid cost function by applying incremental directional corrections, such that it successfully moves from the initial condition (downward pose) to the target position (upward pose) while avoiding the obstacle.
To play the robot arm game, a human player uses a keyboard as the interface to provide directional corrections.
We customize the (up, down, left, right)
keys in a keyboard and associate them with the directional corrections as instructed in the following table.
| Keys | Directional correction | Interpretation |
|---|---|---|
up |
a= [1, 0] | add a counter-close-wise torque to Joint 1 |
down |
a= [-1, 0] | add a close-wise torque to Joint 1 |
left |
a= [0, 1] | add a counter-close-wise torque to Joint 2 |
right |
a= [0, -1] | add a close-wise torque to Joint 2 |
During the game, the algorithm is listening to which key(s) you have hit, and other keys except the above ones will not be recognized. The game procedure is as follows:
- The robot arm plans its motion based on its current control cost function, and then graphically executes the planned motion on the screen.
- While the robot is executing, the human player (you) can press one or multiple key(s) (see the above table instruction) to correct the robot.
- Once the algorithm detects that you have pressed one or multiple keys, the command line will prompt you which key(s) you have hit and also the keystroke time (i.e., the correction time) ---- at which time step in the robot's motion horizon the key is pressed. Then, the interface will translate your key pressing information into the directional correction based on the above table.
- The robot arm will use such the directional correction to update its control cost function. Go to Step 1.
This process repeats until the robot successfully avoids the obstacle and reaches the target position --- mission accomplished!
You can directly run examples/run_quad_game.py to enter the 6-DoF quadrotor game.
$ cd <ROOT_DIRECTORY>
$ python3 examples/run_quad_game.py
Sometimes I meet the following error when running it on macOS.
zsh: illegal hardware instruction
I guess it is caused by the privacy settings of input monitoring. I tried to authorize Python3 for input monitoring but it didn't work. You can either live with it :( or run it in an IDE.
The goal of this game is to let a human player to teach a 6-DoF quadrotor system to learn a valid control cost function by providing directional corrections, such that it can successfully fly from the initial position (in bottom left), pass through a gate (colored in brown), and finally land on the specified target (in upper right).
In the 6-DoF quadrotor game, a human player uses a keyboard as the interface
to provide directional corrections. Specifically, the player can use the (up, down, left, right, a, d) keys, which are
associated with specific directional correction signals, as listed in the following table.
| Keys | Direction correction | Interpretation |
|---|---|---|
up |
T1=1, T2=1, T3=1, T4=1 | Upward force applied at COM of the quadrotor |
down |
T1=-1, T2=-1, T3=-1, T4=-1 | Downward force applied at COM of the quadrotor |
left |
T1=1, T2=0, T3=-1, T4=-0 | Negative torque along y body-axis of the quadrotor |
right |
T1=-1, T2=0, T3=1, T4=-0 | Positive torque along y body-axis of the quadrotor |
a |
T1=0, T2=1, T3=0, T4=-1 | Negative torque along x body-axis of the quadrotor |
d |
T1=0, T2=-1, T3=0, T4=1 | Positive torque along x body-axis of the quadrotor |
During the game, the algorithm is listening to which key(s) you have hit, and other keys except the above ones will not be recognized. The 6-DoF quadrotor game procedure is as follows:
- The quadrotor plans its motion based on its current control cost function, then graphically executing its planned motion on the screen.
- While the quadrotor is executing (flying), the human player (you) can press one or multiple key(s) (see the above table instruction) to correct the quadrotor.
- Once the algorithm detects that you have pressed one or multiple keys, the command line will prompt you which key(s) you have hit and also the keystroke time (i.e., the correction time) ---- at which time step in the robot's motion horizon the key is pressed. Then, the interface will translate your key pressing information into the directional correction based on the above table.
- The quadrotor will use such the directional correction to update its control cost function. Go to 1.
This process repeats until the quadrotor successfully flies from the initial position, passes through the gate, and finally lands on the specified target --- mission accomplished!
If you have encountered an issue in your use of the codes/games (the codes are under regularly update), please feel free to let me known via email:
- Name: Wanxin Jin (he/his)
- Email: wanxinjin@gmail.com
If you find this project/paper helpful in your research, please consider citing our paper.
@article{jin2020learning,
title={Learning from Incremental Directional Corrections},
author={Jin, Wanxin and Murphey, Todd D and Mou, Shaoshuai},
journal={arXiv preprint arXiv:2011.15014},
year={2020}
}