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  • [Music]

  • in the 1960s unum 8 became the first

  • digital and programmable robot to

  • replace humans in an industrial factory

  • in 1961 it was installed at General

  • Motors and it carried out assembly line

  • tasks that were dangerous for humans

  • like picking up hot metals you know mate

  • was basically a big mechanical are much

  • less advanced than some of the tech we

  • have today but even now robots are often

  • built to perform the dangerous mundane

  • or difficult tasks humans perform to do

  • that a lot of these machines are

  • developed to physically replicate our

  • actions and behavior to have things like

  • a bipedal balanced walk a large range of

  • motion and the ability to perceive and

  • interact with the environment but maybe

  • not to your surprise that is a lot more

  • difficult to replicate than some viral

  • robot videos might make you think and

  • without these five groundbreaking

  • inventions much of modern robotics would

  • not be possible one of humanity's most

  • noteworthy traits is our ability to

  • sense our physical environment and react

  • intelligently to that information

  • I mean sometimes our brains are

  • constantly working to synthesize input

  • from things like our eyes nose skin and

  • even internal organs robots however work

  • on a much simpler level in many cases

  • the main information we need them to

  • understand and respond to is visual for

  • instance is the path to their

  • destination open where is something in

  • the way can they climb over that thing

  • or move it to make these decisions

  • robots use machine vision which uses

  • cameras and image processing algorithms

  • to measure and inspect the environment

  • the first image processing programs that

  • were applied to real-life images and

  • environments came around in the 1970s

  • but they were pretty inefficient since

  • then advances with both cameras and

  • image processing have caused some major

  • breakthroughs in machine vision today

  • robots can use multiple 3d and 2d

  • cameras to sense the world around them

  • and those images can be processed and

  • analyzed to detect

  • objects this information is communicated

  • to the brain or the robots centralized

  • computer which then decides based on its

  • programming what action it should take

  • this kind of Tech has allowed robotics

  • to move forward by leaps and bounds and

  • today it's often used on assembly lines

  • for quality control but it also has some

  • more flashy applications for example on

  • an Irving Lee cute robot called pepper

  • uses its machine vision for social

  • purposes it uses complex algorithms to

  • analyze the facial expressions of people

  • around it and it can guess emotions and

  • modify its conversation tactics as a

  • result because of its unique skills

  • pepper is being snapped up for use in

  • customer service to help customers

  • resolve simple easily solved issues a

  • human isn't needed for with machine

  • vision we can build robots that can

  • accurately see and respond to their

  • environment but that alone isn't enough

  • to be practical for robots to really

  • mimic and help us we also need them to

  • monitor their worlds continuously

  • especially if the robot moves that way

  • they can detect if something blocks

  • their path and adjust accordingly and

  • that brings us to our second innovation

  • robots that can monitor their world like

  • this are said to have closed-loop

  • control that means they're constantly on

  • the lookout for changes and can update

  • their internal maps and actions if new

  • obstacles arise now this general idea

  • has been around for a long time like

  • watchmakers we're building things with

  • closed-loop control in the 18th century

  • but it continued to grow as engineering

  • advanced nowadays we have amazing

  • computers with huge processing

  • capabilities and pretty efficient

  • algorithms and they're great at

  • calculating and incorporating feedback

  • this is helpful to stop robots from say

  • running into people but it's also super

  • important for simple things like keeping

  • a robot balanced as it moves the machine

  • needs constant feedback about its weight

  • distribution so that it can adjust its

  • alignment and center of gravity and not

  • fall over which honestly a lot of robots

  • fail at it's really hard for them to

  • navigate precisely and small errors and

  • calculations or coding bugs can cause

  • big problems in locomotion to achieve

  • balance and self

  • the most advanced robots are absolutely

  • decked out in sensors which measure

  • everything from weight to temperature to

  • posture and that helps them achieve

  • their incredibly dexterous human-like

  • movements a great example of this is

  • NASA's Valkyrie robots engineers began

  • developing Valkyrie in 2013 and they

  • originally intended it as an emergency

  • response robot but now it's been

  • redeveloped to one day set up habitats

  • on Mars prior to human arrival to help

  • us Valkyrie would have to perform a

  • number of detailed tasks without any

  • help like picking up boxes and walking

  • up stairs and to do this it is covered

  • in sensors like there are dozens of them

  • in Valkyries hands alone which is why

  • it's fine motor skills are so advanced

  • for a bot so although this thing is a

  • long way from the Red Planet it's still

  • one of the coolest robots being

  • developed today closed loop systems are

  • consistently becoming more advanced as

  • we develop faster more powerful

  • computers to help them with their

  • calculations but of course the computers

  • aren't the only factor here control

  • theory in general is still being

  • developed which means there might be

  • some fundamental lessons left to learn

  • so someday robots like Valkyrie might

  • just be the norm now just because your

  • robot has closed-loop control doesn't

  • mean it can move like a human after all

  • it might be able to adjust its position

  • in real time but that doesn't mean it

  • has the grace that we do reducing that

  • kind of fluid motion is actually one of

  • the unique obstacles for many robots and

  • it's an important one we need robots to

  • have precise motions so they can do

  • tasks that require a little finesse like

  • holding a glass beaker or even shaking

  • someone's hand if you have the power to

  • crush a person's hand you don't want to

  • make a mistake here a lot of robots are

  • getting closer to this fine movement

  • with the help of modern linear actuators

  • an actuator is a mechanical component

  • that converts energy into physical

  • motion and a linear actuator is one that

  • focuses on creating precise forces in a

  • single direction many actuators use a

  • hydraulic or pneumatic force which uses

  • pressurized fluids or gases to create

  • large amounts of energy whatever the

  • last

  • Gator so there have been great

  • improvements in electromechanical linear

  • actuators to ones that use electric

  • motors to produce motion their size cost

  • and energy usage have been significantly

  • reduced compared to other types of

  • actuators and that's allowed us to pack

  • more punch into a smaller area having

  • smaller and more precise actuators

  • allows for more degrees of freedom and

  • finer control of the robotic appendage

  • so by combining these pieces in just the

  • right way you can get a robot that moves

  • with almost eerie fluid grace if you

  • want to see an example for yourself you

  • should watch a video of Boston Dynamics

  • humanoid robot Atlas it uses four

  • hydraulic limbs 28 hydraulic joints and

  • numerous actuators to perform acrobatic

  • jumps somersaults other robot parkour

  • it's pretty cool we will have a link in

  • the description as you might guess

  • modern robots have a lot of working

  • parts and 3d printed ones are becoming

  • more and more commonplace for 3d

  • printing machine parts were generally

  • made either by pouring material into a

  • mold or by removing material to achieve

  • a desired shape but in 2009 a key patent

  • for a type of 3d printing expired and

  • the market began to grow as new

  • companies were finally able to develop

  • and release 3d printers since then this

  • kind of construction has been really

  • picking up steam because of its ability

  • to cheaply and quickly produce uniquely

  • constructed parts with a high

  • strength-to-weight ratio in other words

  • they're both light and strong which is

  • ideal for building robots in 3d printing

  • different ratios are achieved by using

  • filaments with different properties

  • thermoplastics like polycarbonate are

  • often used because the long chains of

  • molecules are durable and very easy to

  • manipulate at high temperatures the

  • chains of molecules are very loosely

  • packed now high temperatures their

  • chemical bonds quickly break down in the

  • material liquefies this pliability

  • allows engineers to precisely design

  • objects and optimize them for

  • load-bearing so their robots can be as

  • sturdy and strong as possible you can

  • find 3d printed parts in all kinds of

  • machines but to go back to a previous

  • example Atlas from Boston dynamic

  • is able to achieve such unique movement

  • partly thanks to its 3d printed parts

  • they allow the bot to be light enough to

  • do all those cool flips finally let's

  • wrap up with one of the most exciting

  • and ongoing advancements in robotics the

  • development of deep learning algorithms

  • these algorithms allow robots to not

  • only sense and react to their immediate

  • environment but also to remember and

  • recall past experiences to help inform

  • future decisions in the past

  • classical programming used static

  • directions to inform robot

  • decision-making rules like if you arrive

  • at a fork in the road always turn right

  • but deep learning algorithms take this

  • one step further they use accumulated

  • experiences to modify their directions

  • for example after turning right and

  • walking into a bunch of walls they might

  • revise their programming to make a rule

  • if you arrive at a fork in the road

  • always turn right unless there is an

  • obstacle the biggest advancement in deep

  • learning is the implementation of neural

  • networks inspired by neurons in the

  • brain these networks are made up of