Artificial Intelligence Instructor: Monica Nicolescu

Artificial Intelligence

Instructor: Monica Nicolescu

Artificial Intelligence 2

Outline

Introduction

– Robotics: what it is, what it isn’t, and where it came from

– Key concepts

• Brief history

• Robot control architectures

– Deliberative control

– Reactive control

– Hybrid control

– Behavior-based control


Key Concepts

• Situatedness

– Agents are strongly affected by the environment and deal

with its immediate demands (not its abstract models)

directly

• Embodiment

– Agents have bodies, are strongly constrained by those

bodies, and experience the world through those bodies,

which have a dynamic with the environment


Key Concepts (cont.)

• Situated intelligence

– is an observed property, not necessarily internal to the

agent or to a reasoning engine; instead it results from the

dynamics of interaction of the agent and environment

– and behavior are the result of many interactions within the

system and w/ the environment, no central source or

attribution is possible


What is Robotics?

• Robotics is the study of robots, autonomous

embodied systems interacting with the physical

world

• A robot is an autonomous system which exists in

the physical world, can sense its environment and

can act on it to achieve some goals

• Robotics addresses perception, interaction and

action, in the physical world


Uncertainty

• Uncertainty is a key property of existence in the

physical world

• Physical sensors provide limited, noisy, and

inaccurate information

• Physical effectors produce limited, noisy, and

inaccurate action

• The uncertainty of physical sensors and effectors is

not well characterized, so robots have no available a priori models


Uncertainty (cont.)

• A robot cannot accurately know the answers to the

following:

– Where am I?

– Where are my body parts, are they working, what are they

doing?

– What did I just do?

– What will happen if I do X?

– Who/what are you, where are you, what are you doing,

etc.?...


The term “robot”

• Karel Capek’s 1921 play RUR (Rossum’s Universal

Robots)

• It is (most likely) a combination of “rabota”

(obligatory work) and “robotnik” (serf)

• Most real-world robots today do perform such

“obligatory work” in highly controlled environments

– Factory automation (car assembly)

• But that is not what robotics research about; the

trends and the future look much more interesting


Classical activity decomposition

• Locomotion (moving around, going places)

– factory delivery, Mars Pathfinder, lawnmowers, vacuum

cleaners...

• Manipulation (handling objects)

– factory automation, automated surgery...

• This divides robotics into two basic areas

– mobile robotics

– manipulator robotics

• … but these are merging in domains like robot pets,

robot soccer, and humanoids


An assortment of robots…


Anthropomorphic Robots


Animal-like Robots


Humanoid Robots

Robonaut (NASA) Sony Dream Robot

Asimo (Honda)

DB (ATR)

QRIO


Outline

• Introduction


– Key concepts

Brief history




– Hybrid control



A Brief History of Robotics

• Robotics grew out of the fields of control theory, cybernetics

and AI

• Robotics, in the modern sense, can be considered to have

started around the time of cybernetics (1940s)

• Early AI had a strong impact on how it evolved (1950s-1970s),

emphasizing reasoning and abstraction, removal from direct

situatedness and embodiment

• In the 1980s a new set of methods was introduced and robots

were put back into the physical world


Cybernetics

• Pioneered by Norbert Wiener in the 1940s

• Combines principles of control theory, information

science and biology

• Sought principles common to animals and

machines, especially with regards to control and

communication

• Studied the coupling between an organism and its

environment


W. Grey Walter’s Tortoise

• Machina Speculatrix” (1953)

– 1 photocell, 1 bump

sensor, 1 motor, 3 wheels,

1 battery, analog circuits

• Behaviors:

– seek light

– head toward moderate light

– back from bright light

– turn and push

– recharge battery

• Uses reactive control, with

behavior prioritization


Braitenberg Vehicles• Valentino Braitenberg (1980)

• Thought experiments

– Use direct coupling between sensors and motors

– Simple robots (“vehicles”) produce complex behaviors that

appear very animal, life-like

• Excitatory connection

– The stronger the sensory input, the stronger the motor output

– Light sensor wheel: photophilic robot (loves the light)

• Inhibitory connection

– The stronger the sensory input, the weaker the motor output

– Light sensor wheel: photophobic robot (afraid of the light)


Example Vehicles

• Wide range of vehicles can be designed, by changing the

connections and their strength

• Vehicle 1:

– One motor, one sensor

• Vehicle 2:

– Two motors, two sensors

– Excitatory connections

• Vehicle 3:

– Two motors, two sensors

– Inhibitory connections

Being “ALIVE”

“FEAR” and “AGGRESSION”

“LOVE”

Vehicle 1

Vehicle 2


Artificial Intelligence

• Officially born in 1956 at Dartmouth University

– Marvin Minsky, John McCarthy, Herbert Simon

• Intelligence in machines

– Internal models of the world

– Search through possible solutions

– Plan to solve problems

– Symbolic representation of information

– Hierarchical system organization

– Sequential program execution


AI and Robotics

• AI influence to robotics:

– Knowledge and knowledge representation are central to

intelligence

• Perception and action are more central to robotics

• New solutions developed: behavior-based systems

– “Planning is just a way of avoiding figuring out what to do

next” (Rodney Brooks, 1987)

• First robots were mostly influenced by AI (deliberative)


Outline

• Introduction


– Key concepts

• Brief history

Robot control architectures



– Hybrid control



Control Architecture

• A robot control architecture provides the guiding

principles for organizing a robot’s control system

• It allows the designer to produce the desired overall

behavior

• The term architecture is used similarly as

“computer architecture”

– Set of principles for designing computers from a collection of well-understood building blocks

• The building-blocks in robotics are dependent on

the underlying control architecture


Robot Control

• Robot control is the means by which the sensing

and action of a robot are coordinated

• There are infinitely many ways to program a robot,

but there are only few types of robot control:



– Hybrid control



Spectrum of robot control

From “Behavior-Based Robotics” by R. Arkin, MIT Press, 1998


Thinking vs. Acting

• Thinking/Deliberating– involves planning (looking into the future) to avoid bad

solutions

– flexible for increasing complexity

– slow, speed decreases with complexity

– thinking too long may be dangerous

– requires (a lot of) accurate information

• Acting/Reaction – fast, regardless of complexity

– innate/built-in or learned (from looking into the past)

– limited flexibility for increasing complexity


Robot control approaches

• Reactive Control

– Don’t think, (re)act.

• Deliberative (Planner-based) Control

– Think hard, act later.

• Hybrid Control

– Think and act separately & concurrently.

• Behavior-Based Control (BBC)

– Think the way you act.


A Brief History

• Deliberative Control (late 70s)

• Reactive Control (mid 80s)

– Subsumption Architecture (Rodney Brooks)

• Behavior-Based Systems (late 80s)

• Hybrid Systems (late 80s/early 90s)


Outline

• Introduction


– Key concepts

• Brief history

• Robot control architectures Deliberative control


– Hybrid control



Deliberative Control: Think hard, then act!

• In DC the robot uses all the available sensory information and

stored internal knowledge to create a plan of action: sense

plan act (SPA) paradigm

• Limitations

– Planning requires search through potentially all possible plans

these take a long time

– Requires a world model, which may become outdated

– Too slow for real-time response

• Advantages

– Capable of learning and prediction

– Finds strategic solutions


Early AI Robots

• Shakey (1960, Stanford Research Institute)

• Stanford Cart (1977) and CMU rover (1983)

• Interpreting the structure of the environment from

visual input involved complex processing and

required a lot of deliberation

• Used state-of-the-art computer vision techniques

to provide input to a planner and decide what to

do next (how to move)


Outline

• Introduction


– Key concepts

• Brief history



Reactive control

– Hybrid control



Reactive Control: Don’t think, react!

• Technique for tightly coupling perception and action to provide

fast responses to changing, unstructured environments

• Collection of stimulus-response rules

• Limitations

– No/minimal state

– No memory

– No internal representations

of the world

– Unable to plan ahead

• Advantages

– Very fast and reactive

– Powerful method: animals

are largely reactive


Vertical v. Horizontal Systems

Traditional (SPA):sense – plan – act

Subsumption:(Rodney Brooks)

“The world is its own best model.”


The Subsumption Architecture

• Principles of design

– systems are built

incrementally

– components are task-achieving

actions/behaviors (avoid-obstacles, find-doors, visit-rooms)

– all rules can be executed in parallel, not in a sequence

– components are organized in layers, from the bottom up

– lowest layers handle most basic tasks

– newly added components and layers exploit the existing

ones


Subsumption Layers• First, we design, implement and debug

layer 0

• Next, we design layer 1

– When layer 1 is designed, layer 0 is

taken into consideration and utilized, its

existence is subsumed

– Layer 0 continues to function

• Continue designing layers, until the

desired task is achieved

• Higher levels can

– Inhibit outputs of lower levels

– Suppress inputs of lower levels

level 2

level 1

level 0

sensors actuators

AFSMinputs outputs

suppressor

inhibitor

I

s


Subsumption Architecture Validation

• Practically demonstrated on navigation, 6-legged

walking, chasing, soda-can collection, etc.


Outline

• Introduction


– Key concepts

• Brief history




Hybrid control



Hybrid Control: Think and act independently & concurrently!

• Combination of reactive and deliberative control

– Reactive layer (bottom): deals with immediate reaction

– Deliberative layer (top): creates plans

– Middle layer: connects the two layers

• Usually called “three-layer systems”

• Major challenge: design of the middle layer

– Reactive and deliberative layers operate on very different

time-scales and representations (signals vs. symbols)

– These layers must operate concurrently

• Currently one of the two dominant control paradigms

in robotics


Reaction – Deliberation Coordination

• Selection:

Planning is viewed as configuration

• Advising:

Planning is viewed as advice giving

• Adaptation:

Planning is viewed as adaptation

• Postponing:

Planning is viewed as a least commitment process

Flakey

TJ


Outline

• Introduction


– Key concepts

• Brief history




– Hybrid control

Behavior-based control


Behavior-Based Control Think the way you act!

• An alternative to hybrid control, inspired from

biology

• Behavior-based control involves the use of “behaviors” as modules for control

• Historically grew out of reactive systems, but not

constrained

• Has the same expressiveness properties as hybrid

control

• The key difference is in the “deliberative” component


What Is a Behavior?

Rules of implementation

• Behaviors achieve or maintain particular goals (homing, wall-following)

• Behaviors are time-extended processes

• Behaviors take inputs from sensors and from other

behaviors and send outputs to actuators and other

behaviors

• Behaviors are more complex than actions (stop, turn-

right vs. follow-target, hide-from-light, find-mate etc.)


Principles of BBC Design

• Behaviors are executed in parallel, concurrently

– Ability to react in real-time

• Networks of behaviors can store state (history),

construct world models/representation and look into

the future

– Use representations to generate efficient behavior

• Behaviors operate on compatible time-scales

– Ability to use a uniform structure and representation

throughout the system


Behavior Coordination

• Behavior-based systems require consistent

coordination between the component behaviors for

conflict resolution

• Coordination of behaviors can be:

– Competitive: one behavior’s output is selected from

multiple candidates

– Cooperative: blend the output of multiple behaviors

– Combination of the above two


Competitive Coordination

• Arbitration: winner-take-all strategy only one response chosen

• Behavioral prioritization

– Subsumption Architecture

• Action selection/activation spreading (Pattie Maes)

– Behaviors actively compete with each other

– Each behavior has an activation level driven by the robot’s

goals and sensory information

• Voting strategies

– Behaviors cast votes on potential responses


Cooperative Coordination

• Fusion: concurrently use the output of multiple behaviors

• Major difficulty in finding a uniform command

representation amenable to fusion

• Fuzzy methods

• Formal methods

– Potential fields

– Motor schemas

– Dynamical systems


Fusion: flocking (formations)

Example of Behavior Coordination

Arbitration: foraging (search, coverage)


Example of representation

• A network of behaviors representing spatial

landmarks, used for path planning by message-

passing (Matarić 90)


Behavior-Based Control summary

• Alternative to hybrid systems; encourages uniform

time-scale and representation throughout the

system

• Scalable and robust

• Behaviors are reusable; behavior libraries

• Facilitates learning

• Requires a clever means of distributing

representation and any potentially time-extended

computation


Robotics Challenges

• Perception

– Limited, noisy sensors

• Actuation

– Limited capabilities of robot effectors

• Thinking

– Time consuming in large state spaces

• Environments

– Dynamic, impose fast reaction times


Lessons Learned

• Move faster, more robustly

• Think in such a way as to allow this action

• New types of robot control:

– Reactive, hybrid, behavior-based

• Control theory

– Continues to thrive in numerous applications

• Cybernetics

– Biologically inspired robot control

• AI

– Non-physical, “disembodied thinking”


Background Readings

• Ronald Arkin, “Behavior-

Based Robotics”, 2001.

Documents

Artificial Intelligence Instructor: Monica Nicolescu