Sensor-Actuator Networks(Braitenburg Vehicles)

“Experiments in Synthetic Psychology”OR

Steps toward “[really] artifical life”

Norm BadlerSteve LaneCSE 377

General Structure

Decision_function

Sensors Actuators

Environment

Actuators = Decision_function(Sensors)

A Simple Example

kS

S V=kS

Environment

V = k(S)Velocity is a linear function of sensor value

Basic Braitenberg Vehicle Design

• Sensor/Actuator Pairs

• Light or other environment feature sensor(s)

• Motor(s) (wheels)

• “Wiring”

Vehicle 1

Two Motors Make it a Little More Interesting (Left-Right Actuators)

kl(Sl) ; kr(Sr)

Sl;Sr Vleft = kl(Sl); Vright = kr(Sr)

Environment

Vleft = kl(Sl); Vright = kr(Sr)Velocity of (left, right) actuators are

linear functions of two sensor values

A Little More Complex

kl(Sr) ; kr(Sl)

Sl;Sr Vleft = kl(Sr); Vright = kr(Sl)

Environment

Vleft = kl(Sl); Vright = kr(Sr)Velocity of (left, right) actuators are

linear functions of two sensor values (but crossed)

Excitatory and Inhibitory Functions

kl(Sl) ; kr(Sr)

Sl;Sr Vleft = kl(Sl); Vright = kr(Sr)

Environment

Vleft = kl(Sl); Vright = kr(Sr)Functions may be excitatory (+) or inhibitory (-)(essentially reflects the slope of the function)

Fear & Aggression

Vehicle 2

Exploring & Love

Vehicle 3

Values & Special Tastes – Con’t

Vehicle 4

• The outer 2 sensors are uncrossed & excitatory

• The next pair in are crossed and excitatory

• The third pair are uncrossed and inhibitory like Sensor/Actuator Pairs

• The fourth pair are crossed and excitatory.

Values & Special Tastes

Vehicle 4

• Dislikes high temperature (turns away from hot places.

• Dislikes light sources (turns toward them and destroys them.

• Prefers oxygenated environment containing organic matter

• Can move elsewhere when O2 & food scarce.

VALUES, KNOWLEDGE & INFERENCE

• From the outside you might conclude that Vehicle 4 has:– a system of VALUES

• Dislikes high temperatures• Dislikes light sources• Prefers environments with food sources

– KNOWLEDGE of its environment and

– an INFERENCE ability • Light bulbs are a source of heat• If I destroy them then I will be cooler• Oxygen & organic matter make energy

But What’s Really Going On?

• Intelligence implies the ability to acquire, represent and process information

• There was no such acquisition or representation of information here.

– In constructing Vehicle 4 we were just playing with the wiring between sensors and actuators

• The behavioral properties and responses that emerge may look intelligent but they really are not.

– When we analyze a system we tend to overestimate its complexity

– Anyone have pets?

Taking this Further

Fl(params,Sl,Sr); Fr(params,Sl,Sr)

Sl;Sr Vleft = Fl(…); Vright = Fr(…)

Environment

Vleft = Fl(params,Sl,Sr); Vright = Fr(params,Sl,Sr)Velocity of (left, right) actuators are non-linear,

parametric functions of two sensor values

Non-linear sensory responses

V= speed of motorI= intensity of stimulation

What’s the Point?

Hard-wired function; Learned function

“Eyes”;“Ears”

“Hunger”

Wheels; Legs; Color;Other internal state

Environment

The decision functions relate actuator behaviorsto the sensed environment.

Can generalize any component.

Decisions ?

Sensor Scope ?

What (Who) is Being Sensed?• Environment

– Check for obstacles, food sources, lights, etc.

– Beware zig-zag wall following…

• Other [nearby] vehicles/creatures

– Check local environment for motion of neighbors, gives rise to flocking and herding behaviors.

– Boids

Some other links

Vehicles: Experiments in Synthetic Psychology, by Valentino Braitenberg (1984), Bradford Books, MIT Press, ISBN 0-262-02208-7

• Braitenberg demos• Braitenberg Vehicles• BEAST• POPBUGS• Gerken (Dewdwey article)• Some implementation notes• You can find a lot more if you Google…

Conclusions• The interaction of simple devices and systems can give rise

to a variety of complex emergent behavior• Many of these individual behaviors can be readily seen in

animals such as insects, bees, ants, etc.– love, fear, aggression, foraging, exploring, etc,

• Group behaviors also can be created in a similar manner– Flocking, herding, schooling, etc.

• You can implement this for particular cases in your worlds.• The computational model is scalable to multiple individuals

(“code re-use”, parameterized).• Lesson Learned – [Graphical] Synthesis is a lot easier than

Analysis!