Crash Course: Artificial Intelligence

John-Green-bot: Hi, Im John-Green Bot and welcome to Crash Course AI!

Today were learning about meeee!!!!

Jabril: Hey!

This is my show!

John-Green-bot: Uh oh Jabril: Its ok John Green Bot, we can do this intro together.

Robotics is a broad topic, because its the science of building a computer that moves and interacts with the world (or even beyond the world, in space).

John Green Bot: So today were going to talk about robots, like me, and what makes us tick!

INTRO Some of the most exciting AIs are robots that move through the world with us, gathering data and taking actions!

Robots can have wings to fly, fins to swim, wheels to drive, or legs to walk.

And they can explore environments that humans cant 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 7 years exploring Mars for us, but it wouldnt be able to build cars like industrial robots or clean your apartment like a Roomba.

Robotics is such a huge topic that its also part of Computer Science, Engineering, and other fields.

In fact, this is the third Crash Course video weve made about robots!

In the field of AI, robotics is full of huge challenges.

In some cases, whats easy for computers (like doing millions of computations per second) is hard for humans.

But with robotics, whats easy for humans, like making sense of a bunch of diverse data and complex environments, is really hard for computers.

Like, for example, in 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 were going to build robots to explore the stars or, get 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 were going to 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.

Lets say you go to a new mall and you want to find some shoes.

What do you do?

You start to build a map of the mall in your head by looking around at all the walls, escalators, shops, and doors.

As you move around, you can update your mental map and keep track of how you got there -- thats localization.

And once you have a mental map and know the way to the shoe store, you can get there more quickly next time.

For example, you can plan that the escalator is faster than the elevator.

The most common way we input that data is with our eyes through perception.

Our two slightly different views of the world allows 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 too, 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, 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-bot, use two cameras to achieve stereoscopic vision like us!

But robots can /also/ have sensors to help them see the world in ways that humans cant.

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.

Infrared depth cameras are how some video game motion sensors work, like how the Microsoft Kinect could figure out where a player is and how theyre gesturing.

This is also a part of how many self-driving cars work, using a technology called LiDAR, which emits over 100,000 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 a streetlamp, 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 a 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 last episode.

For example, lets say John Green Bot had been trained to learn a map of this office, and I wanted him to grab me a snack from the kitchen.

He has localization covered, and now its time to plan.

To plan, we need to define actions, or things that John-Green-bot can do.

Actions require preconditions, or how objects currently exist in the world.

And actions have effects on those objects in the world to change how they exist.

So if John Green Bots mental map has a door between his current location and 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 Bot can go through it.

John Green Bots AI 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 one to take.

Searching through all these possibilities can be really challenging, and there are lots of different approaches we can use to help AIs plan, but that would deserve a video on its own.

Anyway, during planning we run into the third core problem of robotics: manipulation.

What can John Green Bots mechanical parts actually do?

Can he actually reach out his arms and interact with objects in the world?

Many humans can become great at manipulating things (and Im talking about objects, not that force powers stuff).

Like, for example, I can do this but it took me a while to get good at it.

Just look at babies, theyre really clumsy by comparison.

Two traits that help us with manipulation, and can help our robots, are proprioception and closed loop control.

Proprioception is how we know where our body is and how its moving, even if we cant see our limbs.

Lets try an experiment: Im going to close my eyes, stretch my arms out wide, and point with both hands.

Now, Im going to try to touch my index fingers without looking.

Almost perfect!

And I wasnt way off because of proprioception.

Our nervous system and muscles help our bodys 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 control with feedback.

The loop were talking about involves the sensors that perceive whats going on, and whatever mechanical pieces control whats going on.

If I tried that experiment again with my eyes open instead of closed, it would go even better.

As my fingers get closer to each other, I can see their positions and make tiny adjustments.

I use my eyes to perceive, and I control my arms and fingers with my muscles, and theres a closed loop between them -- theyre all part of my body and connected to my brain.

Itd be a totally different problem if there was an open loop or control without feedback, like, for example, if I closed my eyes and tried to touch my finger to someone elses.

My brain cant perceive with their eyes or control their muscles, so I dont 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 or arms so we tighten our grip.

If its EVEN heavier than expected, wed might try & involve our other hand, & if its too heavy, well, well call over my open-loop-example buddy.

But this process has to be programmed when it comes to building robots.

Manipulation can look different depending on a robots hardware and programming.

But with enough work we can get robots to perform specific tasks like removing the cream from an oreo cookie.

Beyond building capable robots that work on their own, we also have to consider how robots interact and coordinate with other robots and even humans.

In fact, theres 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.

Whats so exciting about Robotics is that it brings together every area of AI into one machine.

And in the future, it could bring us super powers, help with disabilities, and even make the world a little more convenient by delivering snacks.

John-Green-bot: Here you go, Jabril!

Thanks, John-Green-bot go get me a spoon.

But were still a long way from household robots that can do all these things.

And when were building and training robots, were working in test spaces rather than the real world.

For instance, a LOT of work gets done on self-driving car AI, before it even gets close to a real road.

We dont want a flawed system 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, well see how teaching AI to play games (even games like chess) can