A Micro-theory of learning Commonsense Motion

 A Micro-theory of learning Commonsense Motion - 

We all know that - 

If I push an object to the left, it will move to the left.

If I push an object to the right, it will move to the right.

If bent/turned along a curve, it will move curvilinearly.

.......

How do we acquire the above commonsense?

When a kid plays with his toy car or blocks, he hits/pushes/throws them in various directions. They move (accordingly). 

One preliminary thing one could say is that the kid has (acquires) a broad sense that wherever he will throw the toy (with the corresponding intention), it wont go in a drastically otherwise manner. That is, if he throws the toy ahead, it wont jump back over his head to his back. Or if he pushes it to the left, it will not jump up. 

So, coming back to the original question of how we learn this, the point is - How does the kid suspect in the first place that there is a correlation between the direction in which he pushes the toy and the direction in which the toy moves? (both of which are only not drastically different).

While moving the toy car, the kid gets a certain sense. It learns something. What?

A kid gets this sense - how I will "CONTROL" the block (car) with my hand, so will it move.

'Intention' + 'Physical Adjustment' (= f(intention)) = 'Control' => 'Way in which the object moves'.

As the kid grows even further, and gets investigative,

Differentials (which are micro-actions and micro-effects) of this above equation are what give the kid the "finer" laws like - 

i) if pushed to left, it moves to left

ii) if turned curvilinearly, it turns along a curve

iii) if lifted upwards, it moves upwards

Lets see these micro-actions / differentials of the above equation : 

d(intention) = slightly intended 'movement', in any sense - say to the left or right, or curvilinear or up or down

+ d(physical adjustment) = a slight push/bend in the corresponding intended sense - to the left/right/down/up/curve

= d(control) = d(intention + physical adjustment)

=> d(way in which the object moves) = slight resultant observed motion of the car - slightly to the left/right/along curve/down/up

The point is this - as the kid grows and matures to do intentional investigations, he observes these above "differential" laws (which are easy for him to test experimentally through micro-actions and seeing over correspondingly short intervals of time, their micro-effects).

Can any 'machine learning' can teach/imbibe this sense into a computer?