Using deep learning and PyTorch to power next gen aircraft at Caltech

Key Takeaways

[Music] caste which stands for the Center for autonomous systems and technology is a new center here at Cal Tech about robotics and autonomous systems when we formed caste we formed along with it five moon shots these moon shots are meant to be representative problems that really expose or crystallize some of the technical challenges that we want to solve through cast that will enable a new generation of autonomous platforms the core premise of caste is that we want to bring together mechanical engineers aerospace engineers computer scientists and electrical engineers to really work very closely together to unify what we call the body in the mind so the body are you know things you think of when you think about hardware and the minor things you think about we think about artificial intelligence our robotics work span multiple hardware and software platforms but we do spend a lot of time with PI torch as one of the AI tools that we use and that we deploy on our robotic platforms the one that's most mature is what's called the neural Lander project well basically is happening there is that we put an ER net to model the aerodynamics of how drone interacts with the ground in order to do smoother landing if you've ever played with a drone yourself you'll know that when you try to land the drone and he gets close to the ground the controller gets very hard to control and oftentimes what happens is you just cut the controller and cut the power to the drone some small distance from the ground and let it just drop and if you ever want to achieve smooth landing what you need to do is actually model the complex interaction to the drone and the ground which you can think of as a barrier or boundary of things and what we've done if we train a neural net to actually model that boundary effect extremely accurately which allows us to do very very smooth and graceful and agile landing and control close to the ground because our goal is to learn the aerodynamics so first step is definitely CAD data it's during the training process I will use different tools and different libraries in potage to have different learning optimization the key technique I'm using is called spectral number Elysian so we will collect bunch of trajectories where the drones close to the ground if we want to learn the ground effect or where the joints close to allergens if we want to learn in the interaction between the tongue and other vehicles after data collection we will train a neural net on pair touch using the server in our lab so in our lab we have some big vehicle which can run near net aambat so I will run the trend neural net on some vehicle for the small vehicle which couldn't read the new neuron bout I will run the neural net on my laptop and we will communicated with a small vehicle then the small vehicle could also get near Network output what these general-purpose open-source platforms like pi torch enables is they enable the ability to have multiple collaborators contribute to a common framework and to quickly and seamlessly interface multiple techniques and modules together then quickly bootstrap and build models and deploy them on our drones in a way that is much more efficient than you know without having a software package like not available I believe that the work that we're doing here at Cal Tech and cast represents a significant step in that direction because we are trying to unify the strengths of multiple fields from machine learning to artificial intelligence aerodynamics and control theory in a way that mitigates the weaknesses of these respective units so we believe that this is a significant step towards achieving that dream you