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visual_dynamics

Algorithms used in the paper Learning Visual Servoing with Deep Features and Fitted Q-Iteration.

The goal is for a quadcopter/drone to follow a car around the city using only image observations. The following images are first-person views of the quadcopter successfully following the car. These are test executions of our policy based on VGG conv4_3 feature dynamics. The executions on the left use the cars seen during training and the ones on the right use novel cars.

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The following executions are longer executions for each of the 5 novel cars.

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Our policy was trained with the fitted Q-iteration algorithm that we propose using only 20 trajectories for reinforcement learning. To see executions of other methods, check out the paper's website.

Installation instructions

Install bleeding-edge version of Theano and apply patch

git clone git://github.com/Theano/Theano.git
cd Theano
git apply patches/theano_matrix_inverse.patch
python setup.py develop --prefix=~/.local

Install bleeding-edge version of Lasagne and apply patch

git clone https://github.com/Lasagne/Lasagne.git
cd Lasagne
git apply patches/lasagne_dilation.patch
pip install -r requirements.txt
pip install --editable . --user

Install OpenCV

sudo apt-get install python-opencv

Install CitySim3D and its dependencies

Follow the instructions from the CitySim3D site.

Install visual_dynamics and its dependencies

git clone [email protected]:alexlee-gk/visual_dynamics.git
cd visual_dynamics
pip install -r requirements.txt

Advanced installation instructions: Use pyenv and install dependencies from source

Set up a new python environment using pyenv

Install desired version of python 3 (e.g. 3.5.2). Make sure to use the --enable-shared flag to generate python shared libraries, which will later be linked to.

env PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install 3.5.2

Install Theano

git clone git://github.com/Theano/Theano.git
cd Theano
pyenv local 3.5.2
python setup.py develop

Install Lasagne

git clone https://github.com/Lasagne/Lasagne.git
cd Lasagne
pyenv local 3.5.2
pip install -r requirements.txt
pip install --editable .

Install OpenCV

Make sure python-dev is installed for the python version being used, e.g.

sudo apt-get install python3.5-dev
git clone [email protected]:opencv/opencv.git
mkdir opencv_build
cd opencv_build
pyenv local 3.5.2
cmake \
-DWITH_CUDA=OFF \
-DCMAKE_BUILD_TYPE=RELEASE \
-DPYTHON3_EXECUTABLE=~/.pyenv/versions/3.5.2/bin/python3.5 \
-DPYTHON3_INCLUDE_DIR=~/.pyenv/versions/3.5.2/include/python3.5m \
-DPYTHON3_INCLUDE_DIR2=~/.pyenv/versions/3.5.2/include/python3.5m \
-DPYTHON_INCLUDE_DIRS=~/.pyenv/versions/3.5.2/include/ \
-DPYTHON3_LIBRARY=~/.pyenv/versions/3.5.2/lib/libpython3.so \
-DPYTHON3_NUMPY_INCLUDE_DIRS=~/.pyenv/versions/3.5.2/lib/python3.5/site-packages/numpy/core/include \
-DPYTHON3_PACKAGES_PATH=~/.pyenv/versions/3.5.2/lib/python3.5/site-packages \
-DINSTALL_PYTHON_EXAMPLES=ON \
-DINSTALL_C_EXAMPLES=OFF \
-DBUILD_EXAMPLES=ON \
-DBUILD_opencv_python3=ON \
../opencv
make -j4
sudo make install
ln -s /usr/local/lib/python3.5/site-packages/cv2.cpython-35m-x86_64-linux-gnu.so ~/.pyenv/versions/3.5.2/lib/python3.5/site-packages/cv2.so

For python 2, the cmake command is the following:

cmake \
-DWITH_CUDA=OFF \
-DCMAKE_BUILD_TYPE=RELEASE \
-DPYTHON2_EXECUTABLE=~/.pyenv/versions/2.7.12/bin/python2.7 \
-DPYTHON2_INCLUDE_DIR=~/.pyenv/versions/2.7.12/include/python2.7 \
-DPYTHON2_INCLUDE_DIR2=~/.pyenv/versions/2.7.12/include/python2.7 \
-DPYTHON_INCLUDE_DIRS=~/.pyenv/versions/2.7.12/include/ \
-DPYTHON2_LIBRARY=~/.pyenv/versions/2.7.12/lib/libpython2.7.so \
-DPYTHON2_NUMPY_INCLUDE_DIRS=~/.pyenv/versions/2.7.12/lib/python2.7/site-packages/numpy/core/include \
-DPYTHON2_PACKAGES_PATH=~/.pyenv/versions/2.7.12/lib/python2.7/site-packages \
-DINSTALL_PYTHON_EXAMPLES=ON \
-DINSTALL_C_EXAMPLES=OFF \
-DBUILD_EXAMPLES=ON \
-DBUILD_opencv_python2=ON \
../opencv

The library can be installed only for the local user by specifying a local install prefix, e.g. -DCMAKE_INSTALL_PREFIX=~/.local, in which case make install should be run without root priviledges and the last symbolic linking step might not needed.

Common installation problems

  • After running cmake, the python2 OpenCV module appears next to 'Unavailable' instead of 'To be built'. Omit the flags that define PYTHON2_EXECUTABLE and PYTHON2_LIBRARY in the cmake command and then fix them with ccmake afterwards.
  • The file Python.h is not found even though it is in the specified PYTHON3_INCLUDE_DIR, fatal error: Python.h: No such file or directory. Explicitly expanding the home directory ~ to ${HOME} might solve this.
  • Installation for python 2 causes the compilation error error: invalid conversion from ‘const char*’ to ‘Py_ssize_t {aka long int}’. In this case, disable python 2 support with the option -DBUILD_opencv_python2=OFF.
  • Importing cv2 gives the error ImportError: dynamic module does not define module export function (PyInit_cv2) because it is using the wrong cv2 library. Make sure the path for the newly built cv2 package appears first in the PYTHONPATH, export PYTHONPATH=~/.pyenv/versions/3.5.2/lib/python3.5/site-packages:$PYTHONPATH.

In Mac OS X, replace the cmake command with this one:

cmake \
-DWITH_CUDA=OFF \
-DCMAKE_BUILD_TYPE=RELEASE \
-DPYTHON3_EXECUTABLE=~/.pyenv/versions/3.5.2/bin/python3.5 \
-DPYTHON3_INCLUDE_DIR=~/.pyenv/versions/3.5.2/include/python3.5m \
-DPYTHON3_INCLUDE_DIR2=~/.pyenv/versions/3.5.2/include/python3.5m \
-DPYTHON3_LIBRARY=~/.pyenv/versions/3.5.2/lib/libpython3m.dylib \
-DPYTHON3_LIBRARY_DEBUG=~/.pyenv/versions/3.5.2/lib/libpython3m.dylib \
-DPYTHON3_NUMPY_INCLUDE_DIRS=~/.pyenv/versions/3.5.2/lib/python3.5/site-packages/numpy/core/include \
-DPYTHON3_PACKAGES_PATH=~/.pyenv/versions/3.5.2/lib/python3.5/site-packages \
../opencv

The option WITH_CUDA=OFF might be necessary if Caffe is used. See this issue for more information.

Links

  1. https://gist.github.com/pohmelie/cf4eda5df24303325b16
  2. http://stackoverflow.com/questions/33250375/compiling-opencv3-with-pyenv-using-python-3-5-0-on-osx

Example usage

Generate training and validation data

mkdir -p data
python scripts/generate_data.py config/environment/simplequad.yaml config/policy/random_quad_back.yaml -n100 -t100 -o data/simplequad_train_data
python scripts/generate_data.py config/environment/simplequad.yaml config/policy/random_quad_back.yaml -n10 -t100 -o data/simplequad_val_data

Train multiscale bilinear dynamics for a particular feature representation

python scripts/train.py config/predictor/multiscale_dilated_vgg_local_level1_scales012.yaml config/transformer/transformer_128.yaml config/solver/adam_gamma0.9_level1scales012.yaml config/data/simplequad.yaml

Learn a weighting of the servoing features using fitted Q-iteration reinforcement learning

python scripts/learn_visual_servoing.py models/theano/multiscale_dilated_vgg_local_level1_scales012/transformer_128/adam_gamma0.9_level1scales012/simplequad/_iter_10000_model.yaml config/algorithm/fqi_nooptfitbias.yaml

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Learning visual servoing with deep features and fitted Q-iteration

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