hi I'm John Green BOTS and welcome to
crash course AI today we're learning
about me hey this is my show oh it's
okay John Green bot we can do the intro
together robotics is a broad topic
because it's the science of building a
computer that moves interacts with the
world or even beyond the world in space
so today we're going to talk about
robots like me and what makes us tick
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some of the most exciting AI is the
robots that can move to the world with
us gathering data and taking actions
robots can have wings to fly fins to
swim
Will's to drive or legs to walk and they
can explore environments that humans
can't even survive in but unlike humans
who can do many different things robots
are built to perform specific tasks with
different requirements for hardware and
for learning curiosity is a pretty
amazing robot who spent seven years
exploring Mars for us but it wouldn't be
able to build cars like industrial
robots or clean your apartment like a
Roomba robotics is such a huge topic
that it's also a part of computer
science engineering and other fields in
fact this is the third crash course
video we've made about robots in the
field of AI robotics is full of huge
challenges in some cases what's easy for
computers like doing millions of
computations per second is hard for
humans but with robotics what's easy for
humans like making sense of a bunch of
diverse data in complex environments is
really hard for computers like for
example and the reinforcement learning
episode we talked about walking and how
hard it would be to precisely describe
all the joints and small movements
involved in a single step but if we were
going to build robots to explore the
Stars or give me a snack we have to
figure out all those details from how to
build an arm to how to use it to grab
things so we're gonna focus on three
core problems in robotics localization
planning and manipulation the most basic
feature of a robot is that it interacts
with the world to do that it needs to
know where it is which is localization
and how to get somewhere else which is
planning so localization and planning go
hand-in-hand we humans do localization
and planning all the time let's say you
go to a new mall and you want to find
some shoes what do you do you start to
build a map with the mall in your head
by looking around at the walls escalator
shops and doors as you move around you
can update your mental map and keep
track of how you got there
that's localization and once you have a
mental map and know the way to the shoe
store you can get there Marc quickly
next time so maybe you
escalators faster than the elevator the
most common way we input that data is
with our eyes and perception our eyes
give us two slightly different views of
the world which allow us to see how far
away objects are in space this is called
stereoscopic vision and this mental map
is the key to what many robots do to if
they need to move around the world as
they explore they need to simultaneously
track their position and update their
mental map of what they see this process
is called simultaneous localization and
mapping which goes by the cool nickname
slam but instead of eyes we robots use
all kinds of different cameras many
robots use RGB cameras for perception
which gather color images of the world
some robots like John Green BOTS used
two cameras to achieve stereoscopic
vision like us but robots can also have
sensors that help them see the world in
ways that humans can one example is
infrared depth cameras these cameras
measure distances by shooting out
infrared light which is invisible to our
eyes and then seeing how long it takes
to bounce back and Fred depth cameras
are how some video game motion sensors
work like how the Microsoft Kinect could
figure out where a player is in how
their gesturing this is also part of how
many self-driving cars work using a
technology called lidar which emits over
a hundred thousand laser pulses a second
and measures when they bounce back this
generates a map of the world that marks
out flat surfaces and the rough
placement of 3d objects like street
lamps a mailbox or a tree on the side of
the road once robots know how close or
far away things are they can build maps
of what they think the world looks like
and navigate around objects more safely
with each observation and by keeping
track of its own path a robot can update
its mental map but just keep in mind
most environments change and no sensor
is perfect so a lot goes into
localization but after the robot learns
about the world it can plan paths to
navigate through it
planning is when an AI strings together
a sequence of events to achieve some
goal and this is where robotics can tie
into symbolic AI from the last episode
for example let's say John Green bot had
been trained to learning map of this
office and I wanted him to grab me a
snack from the kitchen
yes localization covered
now it's time to plan to plan we need to
define actions or thinks that John Green
bot can do actions require preconditions
or how objects currently exists in the
world and actions have effects on those
objects to change how they exist so if
John Green BOTS mental map has a door
between his current location in the
kitchen he might want to use an open
door action to go through it this action
requires a precondition of the door
being closed and the effect is that the
door will be open so that John Green
BOTS can go through it John Green BOTS
say I would need to consider different
possible sequences of actions including
their preconditions and effects to
reason through all the routes to the
kitchen in this building and choose
which one to take
searching through all these
possibilities can be really challenging
and there are lots of different
approaches we can use to help a ice plan
but that would deserve a video on its
own anyway during planning we run into
the third core problem of Robotics
manipulation what King John Green BOTS
mechanical parts actually do can he
reach out his arms and interact with
objects in the world many humans can
become great at manipulating things and
I'm talking about objects not that force
power stuff like for example I can do
this but it took me a while to get good
at it I mean just look at babies they're
really clumsy by comparison two traits
that help us with manipulation and it
can help our robots are proprioception
and closed-loop control proprioception
is how we know where our body is and how
it's moving even if we can't see our
limbs let's try an experiment I'm going
to close my eyes stretch my arms out
wide and point with both hands now I'm
gonna try and touch both index fingers
without looking almost perfect and I
wasn't way off because of proprioception
our nervous system and muscles help our
body sense of proprioception but most
robots have motors and need sensors to
figure out if their machine parts are
moving and how quickly the second piece
of the puzzle is closed loop control or
a control with feedback the loop we're
talking about involves the sensors that
perceive what's going on and whatever
mechanical pieces control what's going
on if I tried that experiment again with
my eyes open instead of clothes it would
go even better
as my fingers get closer to each other I
can see their positions and make tiny
adjustments
I used my eyes to perceive and I control
my arms and fingers with my muscles and
there's a closed loop between them
they're all part of my body and
connected to my brain it'd be a totally
different problem if there was an open
loop or control without feedback like if
I closed my eyes and tried to touch my
fingers to someone else's my brain can't
perceive with their eyes or control
their muscles so I don't get any
feedback and basically have to keep
doing whatever I start doing we use
closed loop control in lots of
situations without even thinking about
it if a box were picking up is heavier
than expected we feel it pull the skin
on our fingers so we tighten our grip
and if it's even heavier than expected
we might use our other hand and if it's
still too heavy well we'll call over our
open loop example buddy but this process
has to be programmed when it comes to
building robots manipulation can look
different depending on a robot's
hardware in programming but with enough
work we can get robots to perform
specific tasks like removing the cream
from an Oreo beyond building capable
robots that work on their own we also
have to consider how robots interact and
coordinate with other robots in even
humans in fact there's a whole field of
human robot interaction that studies how
to have robots work with or learn from
humans this means they have to
understand our body and spoken language
commands what's so exciting about
robotics is that it brings together
every area of AI into one machine and in
the future it could bring us superpowers
help with disabilities and even make the
world a little more convenient by
delivering snacks
[Applause]
thanks John Green BOTS go give me a
spoon but we're still a long way from
household robots that can do all of
these things and when we're building in
training robots were working in test
spaces rather than the real world for
instance a lot of work gets done on
self-driving car a guy before it even
gets to an actual road
we don't want flawed systems to
accidentally hurt humans these test
spaces for AI can be anything from
warehouses where robots can practice
walking to virtual mazes that can help
an AI model learn to navigate in fact
some of the common virtual test spaces
are programmed for human entertainment
games so next week we'll see how
teaching AI to play games even games
like chess can help us solve real-world
problems see you then crash course is
produced in association with PBS Digital
Studios if you want to help you crash
course free for everyone forever you can
join our community on patreon and if you
want to learn more about engineering
robots check out this video
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