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+<h2>Model-Driven Engineering of Intelligent Robot Architectures</h2>
+<h3>Paper accompanying website</h3>
+
+<a href="http://www.se-rwth.de/~kusmenko/">Evgeny Kusmenko</a>, Svetlana Pavlitskaya
+<a href="http://www.se-rwth.de/~rumpe/">Bernhard Rumpe</a>,
+and Thomas Timmermanns
+
+<br><br>
+
+<b>Contents for Supplementary Material according to Paper Outline:</b><br>
+<a href="#IN">1 Intro</a><br>
+<a href="#RW">3 Related Work</a><br>
+<a href="#MA">4 MontiAnna</a><br>
+<a href="#CNC">5 AI Driven Robot Architectures</a><br><br>
+Video demonstration:
+<iframe width="640" height="360" src="https://www.youtube.com/embed/XDeITQlUnA0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+<br><b>
+Detailed instructions on how to use EmbeddedMontiArc and MontiAnna to create a deep learning based robot software are given in
+<a href="https://git.rwth-aachen.de/autonomousdriving/torcs_dl/tree/develop">here</a>.</b>
+<br><br>
+
+<a name="IN"></a>
+<h4>1 Intro</h4>
+
+<a href="https://rwth-aachen.sciebo.de/s/5LJgqqhyTgEiepU">EmbeddedMontiArc Studio - preconfigured Ubuntu VM</a>.
+The EmbeddedMontiArc Studio is a web based IDE for EmbeddedMontiArc. It feature a sample project defining a neural network in
+MontiAnna. To get seeing the first results quickly, the network is trained on the cifar-10 data set which takes several minutes to train
+on a CPU or less than a minute on a GPU.<br>
+Please follow the instruction on the desktop to run the project or watch <a href="https://www.youtube.com/watch?v=KK85jvMp55s">this video</a>. You are invited to customize the network or retrain it using your own labeled images.
+<a name="RW"></a>
+<h4>3 Related Work</h4>
+
+The running example used in the paper to compare the deep learning frameworks is the so called
+ResNet152 deep network. It is a convolutional neural network able to learn to extract
+information from images, for instance to make a robot understand its environment. 
+Residual networks such as the ResNet152 try to tackle the vanishing gradient problem in deep architectures
+by introducing so called residual blocks.
+<br><b>
+<a href="resnet152.rar" target="_blank">
+The implementations of the ResNet152 we used for our framework comparison are available
+  
+here
+   </a>.</b><br>
+In addition to the actual code, each implementation contains its origin as well as some remarks.
+Note that there are always many ways to define a complex architecture such as the ResNet152. 
+Hence, shorter or more elegant examples might exist. However, we may conclude that using most frameworks the
+ResNet152 needs several hundresds of lines of code. Caffe even needs over 6000 lines of codes. <br><br><b>The MontiAnna
+description of ResNet152 contains only 33 lines of code!</b> This is one order of magnitude less than the other languages need.
+<br><br>
+Further network examples, e.g. the AlexNet can be found in our <a href="https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/languages/CNNArchLang/tree/master/src/test/resources/architectures">test model repository</a>.
+<br>
+
+
+<a name="MA"></a>
+<h4>4 MontiAnna</h4>
+
+MontiAnna is the umbrella term for a set of frameworks containing two languages and several code generators.
+The modules are:
+<ul>
+<li><a href="https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/languages/CNNArchLang" target="_blank">CNNArc</a>: the language to define
+the architecture of a deep artificial neural network. As the framework was originally intended for CNNs, the name still contains the term. 
+However, MontiAnna can handle any kind of deep layered networks.</li>
+<li><a href="https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/languages/CNNTrainLang" target="_blank">CNNTrain</a>: the training language to define
+the training hyperparameters, loss function, etc.
+</li>
+<li><a href="https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/CNNArch2MXNet" target="_blank">CNNArch2MXNet</a>: The MontiAnna-to-MxNet
+compiler. <a href="https://mxnet.apache.org/">MxNet</a> is a widely used deep learning framework used in industry applications, e.g. by Amazon.
+Our compiler produces C++-code for deployment and Python code for training. Furthermore, we provide CMAKE files to facilitate the final
+compilation process of the generated MxNet code.
+</li>
+</ul>
+
+<a name="CNC"></a>
+<h4>5 AI Driven Architectures</h4>
+We embed MontiAnna into a component and connector (C&C) modeling language called EmbeddedMontiArc. EmbeddedMontiArc
+allows us to decompose software architectures hierarchically and to implement the components using MontiMath, a Matlab-inspired
+matrix-based language for math-heavy algorithms such as controllers (PID, MPC,...).
+
+Having composed EmbeddedMontiArc with MontiAnna we make it possible to use neural networks as an alternative to MontiMath based 
+component implementaions.<br> 
+The composed language governing the sub-languages is 
+<a href="https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/languages/EmbeddedMontiArcDL">EmbeddedMontiArcDL</a>.<br>
+The composed generator compiling the architecture model, the MontiMath behavior, as well as MontiAnna deep neural networls to C++ code as well as corresponding CMAKE files
+for building the executable is <a href="https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMADL2CPP">EMADL2CPP</a>.
+<br><br>
+To demonstrate our methodology in action, we developed a self-driving vehicle software based on the direct perception principle.
+<br><b>
+Detailed instructions on how to use EmbeddedMontiArc and MontiAnna to create a deep learning based robot software are given in
+<a href="https://git.rwth-aachen.de/autonomousdriving/torcs_dl/tree/develop">here</a>.</b>
+<br><br>
+<b><a href="https://git.rwth-aachen.de/autonomousdriving/torcs_dl/blob/develop/doc/deep_driving_project.zip">The complete project is available here.</a>
+The model sources can be found under src/main/dp. The main component is stored as plain text in the Mastercomponent.emadl file. Subcomponents can be found 
+in the subdirectory subcomponents. In particular the deep learning component is stored in <b>Dpnet.emadl</b> and its training is specified
+in <b>Dpnet.cnnt</b>.
+</b>
+<br>
+The system uses a deep neural network to extract a dozen of scalar affordance indicators from a front camera image. The affordance indicators
+are scalar quantities, e.g. the distance to the front car, the distance to the lane marking, etc.
+<br>
+The affordance indicators extracted by the neural network are then fed into Kalman filters and a controller written in MontiMath which in turn computes
+the actuator commands, i.e. steering, acceleration, and braking.
+<br>
+The graphical component and connector architecture of the autonomous vehicle is depicted below. The Deep Leaning component is highlighted in violet. 
+Other components are either implemented in MontiMath or are a composition of subcomponents. Of
+course it is possible to have multiple deep learning components in an architecture:<br>
+<img src="cnc.png"></img>
+
+The workflow of the system design with EmbeddedMontiArc + MontiAnna is depicted in the following diagram.<br>
+<img src="workflow.png"></img>
+<br><br>
+The generation workflow is depicted next:
+<img src="generators.png"></img>
+<br><br>
+
+Of course, EmbeddedMontiArc+MontiAnna can be used to design a variety of other robotics applications beside the autonomous driving domain.
+
+

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