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>> By now, you've probably guessed
that this is no ordinary boat trip.
And this is no toy.
It's called an IVER, an autonomous underwater vehicle,
but members of the applied research lab
at Penn State aren't really working on the vehicle.
They're more concerned with its mind.
>> So, we're going to have it drive on the surface initially
at a 50-degree heading and when it picks up speed,
then we're going to start diving.
It's going to hold a 7-degree downward pitch
until it reaches its target depth and then it's going
to level out and try to maintain that depth.
>> The team lead by John Sustersic wants
to build a self-driving submarine,
but this project is a little more complicated than that.
>> We're trying to develop an autonomous system,
an unmanned system that can interact with people as well
as a manned system would.
And so the number of, you know, technical theoretical challenges
that have to be overcome there is pretty big.
>> The system is called MANTA, multi-agent architecture
for natural and trusted autonomy.
Let's start with the multi-agent part first.
It's built to mimic the command structure
of an actual submarine.
There's a commanding officer, a navigator, officer of the deck,
a pilot, and an engineer and these autonomous agents
or computer programs use the same language
that you would hear on a sub.
So, when the officer of the deck tells the pilot to dive,
it uses the word dive.
>> By allowing the agents to interact via natural language,
then an outsider observing the operation
of the system will understand the high-level decision making
that's in play and really we can have humans intermixed
with the autonomous agents.
So, you could have a human commanding officer issuing
orders to the rest of the autonomous team.
>> That's the ultimate goal.
A trusted autonomous system that can operate
without any humans or just a few.
To build it, Sustersic built a diverse team,
one that includes engineers,
programmers, and a mathematician.
>> Most of what I'm looking at has to do
with the learning aspect.
We have right now an entire architecture devoted to logic.
Right? But if you really think
about what makes an organism robust in a situation,
one of the most important components is that its adaptable
and that adaptability requires learning.
What I'm thinking about is how do we take in sort
of the raw data of the world and how do we learn from it
so that we have a platform that can adjust
to novel circumstances.
>> It's a problem Sustersic compares
to teaching a teenager how to drive.
Safety comes with experience.
>> Our belief is that by enabling learning
in that constrained way, then the system will become better
at doing what it does and so we'll be able
to relax the constraints and give it more decision authority
over time, in which we would keep everyone happy.
>> At the lake, the team sets clear constraints
and objectives before any mission begins.
>> Going to come back up, get a GPS fix, come back around,
parallel reverse course, then take another swath of imagery.
>> The results can be checked and tweaked onsite.
They can also be duplicated
in the lab using a popular video game engine.
>> So, this is our MANTA simulator.
It is actually a two-part simulator.
All we do is put it in the environment
and we have the soar agents command what our vehicle is
doing and we can see real time what it looks like.
And we also have a visualizer which takes in data
from physical tests, like the ones out of the lake,
and we can put that data in here and actually replay the mission
that took place out there on a screen.
>> This continual testing is vital
to developing agents capable of completing complex tasks
and building the trust needed for human interaction.
>> So many of the challenges that the autonomous system has
to face is is it safe to go here, how much risk do I assume
if I take this particular path versus that particular path.
Those are the things that people are doing all the time
and things that this system needs to demonstrate
to people's satisfaction that it will make reasonable decisions.
>> When you think about computers, they are part
of a long line of technology that's designed
to do all the things that humans are bad at.
They are designed to record information perfectly.
They're designed to compute very quickly, but the current age
of technology is interested in doing the things
that humans are good at.
It's sort of working with fuzzy information,
it's transferring knowledge from one domain to another.
>> And when it comes to that transfer,
the team at ARL is just beginning
to scratch the surface.