CAST is the Center for Autonomous System Technology at Caltech. It's a place where
Caltech and JPL scientists and students and researchers come together in order
to define a new vision for researching autonomous systems from basic science to applications.
Right now there's a big push towards autonomy in general -
autonomy on robotic systems, autonomy in cars. You have machine-learning, robotics,
computer vision, all of these areas coming together, and CAST is a way of
really trying to address central problems in this theme in a way that's
interdisciplinary in an environment that can't really be duplicated at other universities.
My area of research is artificial intelligence and machine learning,
and more specific to CAST are these areas of reinforcement learning.
I've been talking to Aaron Ames about having these bipedal robots be able to
adapt to any kind of surface and ultimately be autonomously able to walk.
One of the challenges of getting robots to work robustly in the wild is that it
needs to be able to deal with unforeseen circumstances and unknown environments.
So, in the lab everything is well-controlled, everything is well-sensed, and
we know everything there is to know. In order to deal with a lot of
heterogeneity, we need to be able to develop a better mind to put into these robots.
Mind is where I come in. I work in artificial intelligence and machine learning
and so I'm gonna put the mind into the body. It's not just making
fancier drones and robots or developing software that works alone, but the point
is to bring these two together in order to make them more effective in helping
the human scientists to do their job better.
We've been using drones and other UAVs to study Earth's surface processes in a
range of different places. Technology is enabling a couple of different things.
One, the ability to explore environments that are just impossible for us to go,
for example the subsurface oceans on some of Jupiter's moons.
Additionally there are all kinds of places that we would like to be all the time,
but we can't be because we're here. We have to multitask, and there are
interesting and important things happening in the environment - we want
the opportunity to discover those. I think what is going to make CAST special
is a partnership where we're actually going to be able to start with questions
that are motivated by scientific problems. We're not just grabbing
off-the-shelf technologies, we're going to develop approaches that actually are
well-suited to specific types of science problems. The CAST facilitiy is actually
remarkable, right? We have this amazing wind tunnel: I could generate incredibly
complex wind conditions to be able to test some of these challenges, and
downstairs, professor Chung has a state-of-the-art lab where they simulate
micro-friction. I'm standing in to my new spacecraft research lab where we are
going to have our frictionless spacecraft simulator. Basically they are
floating on the very high precision surface and where we can actually
implement advanced guidance control and autonomy algorithms. By the end of this
year, we'll have a five spacecraft simulators for swarms of space craft
research. The idea is that you have this collection of a spacecraft work
together so that they can form a certain particular shape. Flying one drone -
maybe you have to worry about collision with respect to the obstacles,
but now you have maybe two or maybe thousands of these drones, then you also
have to consider collision avoidance, so it becomes a little more complicated.
Once you figure out the math and the algorithmic detail, then you can actually
achieve all these benefits of these swarm technology.
In the tradition of Caltech, we are in the business of taking the high-risk challenges.
From LIGO to landing on Mars, we have not been deterred by the level of challenges so
CAST is, in the same tradition, going to really embark on solving challenges in
autonomous system technologies. This is a long-term dream that has come true.