Nowadays, robots are everywhere, their importance is growing in science,  society and other industries.
They appear in all fields, factories, space,  sea depths, gardens and in living rooms.
Some people think that robots in the 21st century they'll be a bit like cars in the 20th century .
Robots haven't just entered the industry, they are taking part in our culture's transformation, and sometimes,  they even take part in how we understand ourselves as humans, in a new way.
So, what's a robot?
First of all, a robot is a physical machine  provided with a number  of engines to produce actions or to produce movements
it also has detectors or sensors as we call them that enable it to perceive things  in its environment,
for example, a camera to see images, micros to detect sounds,  force sensors to sense when it touches an obstacle or when it is bumped into something.
for example infrared sensors to detect things that humans can't see, but animals can.
The most important definition of a robot is that information is gathered with the environmental sensors, it is used to decide which movements the robot will produce.
So there is a permanent interaction between perception and action,  that's what defines robots.
As opposed to automatons,  for example, Jaquet-Droz or Vaucanson's automatons in the 18th century,  which were amazing machines.
Nevertheless, the action didn't depend on what was going on around them,  they always did the same thing,  regardless of what was happening around them.
A robot doesn't operate like that, it reacts differently according  to what is happening.
So in practice, this definition of robots covers a wide range of forms of robots,it can be imaginary robots,  humanoid or animaloid robots, but in the end, today,   they can only be seen in science labs.
This also concerns a lot of robots we live with nowadays
planes are widely automated, cars have driver-assistance system modes,  there are robot vacuum cleaners or machines that will mow the lawn autonomously,
some toys too, some fun robots in toy shops, some of which are provided with very advanced technologies.  And this diversity of forms covers a rather wide variety of content, operation and mechanism too.
So we can talk about a few great dimensions that enable to differentiate mechanisms.
the first dimension is the autonomy.
Some robots whose behaviour is autonomous because actions are done while it is perceiving its environment without permanent instructions or sometimes just a partial instruction, given from humans.
For example, robots in an automobile factory which assemble vehicles,  sometimes with other robots,  well if humans don't interfere in the assemblingand robots do it alone,  they will be called autonomous robots.
However, robots in nuclear plants,  which are operated by operators   to go in radioactive confinement rooms  where the operator has a remote control
that allows him to define which actions they have to do at each moment, those robots aren't autonomous.
Another dimension that differentiates several families of robots,   is adaptation and learning.
Some robots' performance is completely frozen beforehand by a program and whatever the robot will experience in its environment,   it will always behave the same way.
On the other hand, there are other robots equipped with  other family mecanisms  called learning algorithms,  ,
that enables them to update their action modes and strategy actions according to the events they sense in the environment, according  to tests and mistakes they will do.
So, for example, there are robots that can explore an environment and gradually build up a map  of this environment that will enable them  to navigate in it in a rather efficient way  although at first, they couldn't.
Some mecanisms will allow them to learn  to recognize new objects a human will name or for example to learn to recognize  humans' actions or to  learn how to move their legs like in a locomotion robot
These mecanisms are called learning algorithms, they enable machines to detect regularities    in what they experience,  an experience is made  when testing an action and observing the result.
It can be repeated a certain number of times.
When detecting regularities, they can understand  how to predict the result   of their action, for example.  Concerning how to find a solution,
a behavioral strategy, for example to learn how to walk,  there are test-mistakes strategies   where algorithms  called optimization algorithms
will enable them to gradually refine the settings   of these strategies to progressively  improve these solutions efficiency.
In a number of cases, the solutions found by these machines are quite surprising  or weren't predicted well by the engineer  who designed the robot  or the learning algorithm.
This doesn't mean these robots will invent completely unthinkable things, but for example, it can be a locomotion strategy, a way of using one's body to move which can seem a little absurd for a human.
But observed carefully, it is a very efficient strategy,and by using test-mistakes, it's the robot who found it.
There can also be learning algorithms a little more sophisticated that encourage machines to explore an environment and to search for novelty or information to enrich their knowledge.
This will allow a machine to develop its know-how list   which wasn't completely  programmed beforehand.
Nevertheless, we must be careful when saying this,    because we could get the impression  that the machines are going to be  quite intelligent and creative,
but in fact they are still extremely far  from adaptation capacities  of humans, of animals even,  and of more simple mammals.
There's still a great amount of work to do before being able  to design machines which have the adaptation  capacities of a 3 or 4 month-old child.