Python library for simulating dynamics and Koopman-based learning of dynamical models.
The code in this repository was prepared to implement the methodologies described in
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C. Folkestad, D. Pastor, I. Mezic, R. Mohr, M. Fonoberova, J. Burdick, "Extended Dynamic Mode Decomposition with Learned Koopman Eigenfunction for Prediction and Control", in Proc. American Control Conf, (accepted) 2020
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C. Folkestad, D. Pastor, J. W. Burdick, "Episodic Koopman Learning of Nonlinear Robot Dynamics with Application to Fast Multirotor Landing", in Proc. International Conference on Robotics and Automation, (accepted) 2020
The simulation framework of this repository is adapted from the Learning and Control Core Library.
Set up virtual environment
python3 -m venv .venv
Activate virtual environment
source .venv/bin/activate
Upgrade package installer for Python
pip install --upgrade pip
Install requirements
pip3 install -r requirements.txt
To run the code that demonstrates the method described in [1], run one of the examples
core/examples/inverted_pendulum.py
core/examples/cart_pole.py
Each example:
- Collects some data with a stabilizing controller
- Compute principal eigenvalues
- Fit the diffeomorphism using PyTorch
- Fit a linear model using SkLearn
- Test prediction error
- Test closed loop error
To run the code that demonstrates the method described in [2], run the example
core/examples/episodic_1d_landing.py
Run the example scripts as a module with the root folder of repository as the working directory. For example, in a Python 3 environment run
python -m core.examples.cart_pole.py