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Adaptive Robotics

COM2110

Autumn Semester 2008

Lecturer: Amanda Sharkey

“Robot”

the word “robot” comes from the play
`Rossum`s Universal Robots`, by
Czech writer Karel Capek (1921)


Robot, from robota, “servitude,
forced labour, drudgery”


Robots rebel, and kill all humans

What is a robot?



Joseph Engelberger, a pioneer in
industrial robotics: "I can't define a
robot, but I know one when I see
one."



Brady (1985)


“the intelligent connection of
perception to action”


Arkin (1998)

“An intelligent robot is a machine able
to extract information from its
environment and use knowledge
about its world to move safely in a
meaningful and purposive manner”


Robotics Industry Association:


“a robot is a re
-
programmable, multi
-
functional, manipulator designed to move
material, parts, tools or specialised
devices through variable programmed
motions for the performance of a variety
of tasks”

(excludes mobile robots!)



Changing definitions




Stop fearing the robot


stop making a man of him! Just
remember that the sewing machine is a robot, the automobile is
a robot, the electric car and the phonograph and the telephone
are all robots. Each one men have developed in order to
unburden themselves of some onerous task and on to better
things. Each one does a specific job, and no more. Why begin
now to worry about robots when we have been enjoying their
services for centuries?




Woodbury, D. (1927)
Dramatising the
“robot”,

New York Times, Nov 1st


Like Wittgenstein and “games”


No single feature shared by the many
examples, but rather “
a complicated
network of similarities, overlapping and
criss
-
crossing
” [Wittgenstein, 1953].


The same is also true of ‘robot’


the
various examples bear family
resemblances rather than a single
meaning.


Different groups of robots


Autonomous robots


Industrial robots


Human
-
like robots


Self
-
configurable robots


Biological models


Toys and companions

Course Aims


To present the key concepts of a recent
approach to AI


And contrast to earlier approaches


To consider the underlying mechanisms
for robot control


To inform about research in robotics


What are the motivations?


Applications


Biological inspiration


Biorobotic modelling


Understanding intelligence



Teaching Method

Lectures, and assignment.

See website for course (Lecturer’s
module pages)


Assessment: Exam and assignment


Background Reading

Clark, A. (1997) Being There: Putting Brain, Body
and World Together Again. A Bradford Book,
MIT Press

Franklin, S. (1995) Artificial Minds: A Bradford
Book, MIT Press

Nolfi, S. and Floreano, D. (2000) Evolutionary
Robotics: The biology, intelligence and
technology of self
-
organising machines. A
Bradford Book, MIT Press

Pfeifer, R., and Scheier, C. (2001) Understanding
Intelligence, MIT Press



Why robotics?



Can we create artificial beings?


Are we machines?


How do we work?


Understanding by building



Making robots to perform useful tasks


Robots as companions?


What is Adaptive Robotics?

Recent approach to AI


Reflected in


Behaviour based robotics


Reactive robotics


Evolutionary robotics


Artificial Life


Swarm Intelligence and swarm robotics


Embodied cognition



Different views of mind and
cognition


Emphasis on Mind and Reasoning
independent of world (
computationalism
)

How can mind emerge from the workings of
a physical machine? (brain)
(
connectionism
)

Relationship between brain, body, mind and
world…. (
embodied cognition
)



Three Stage Progression to current
emphasis on Embodied Cognition

1.
Classical Cognitivism or
computationalism (late 1950’s to
1980’s)

2.
Connectionism (main period


1980’s)

3.
Embodied Cognition and Adaptive
Intelligence (1990’s to present)


N.B. dates only a rough guide

1. Computationalism


Mental states = computational states

Good Old Fashioned Artificial Intelligence
GOFAI


Physical Symbol System Hypothesis (Newell
and Simon, 1976)

A physical symbol system is a necessary and
sufficient condition for general intelligent action.


intelligence is symbol manipulation


computers manipulate symbols


computers can be intelligent

1. Computationalism cont.


Memory as retrieval from stored
symbolic database


Problem solving as logical inference


Cognition as centralised


Environment just a problem domain


Body as an input device

Shakey


Functionalism

“The mind is to the brain as the
program is to the hardware”
(Johnson
-
Laird, 1988)


-

hardware/software distinction

-
we are interested in the software


could run on any hardware (Swiss
cheese?)


2. Connectionism


Neural nets


An account of mental states in terms
of neurons


related to brain


Memory as pattern recreation


Problem solving as pattern
completion and transformation


Cognition


decentralised


3. Embodied Cognition

As connectionism PLUS



Environment as active resource


Body as part of computational loop


Brain, body, world intricately
interconnected

3. Embodied cognition cont.


Gradual move away from
anthropocentric view


Greater awareness of abilities of
non
-
human organisms, and their
abilities to interact with and survive
in the world.

Early mobile robots: Shakey

Shakey the Robot


Developed by SRI (Stanford Research
Institute) from 1966
-
1972


First mobile robot to visually interpret,
and reason about its surroundings


TV camera, range finder, bump sensors


Programs for sensing, modelling and
planning


Example task: “push the block off the
platform”


Stanford Cart


TV cameras: took pictures of scenes,
and planned path between obstacles




Sense


Model


Plan


Action



Brooks:1991

“Intelligence without representation”


Realisation that mobility, vision and
ability to survive are important
aspects of intelligence



Brooks and idea of Creatures


Able to cope with changing and
uncertain world


Should have goals, and purpose in
being


“An ant, viewed as a behaving system, is quite
simple. The apparent complexity of its
behavior over time is largely a reflection of the
complexity of the environment in which it
finds itself”


Herbert A. Simon, 1969


Idea of
reactive
responses to the world, instead
of modelling and planning.


Intelligence is determined by the dynamics of
interaction with the world.




Key concepts in new
approach to AI


a) Reactive behaviour


b) Adaptivity


c) Situatedness


d) Embodiment


e) Emergence and Self
-
organisation


Changing view of intelligence


a. Reactive Intelligence

Arkin (1995): hallmark characteristics

-
emphasis on behaviours and simple
sensorimotor pairings

-
Avoidance of abstract representational
knowledge (time consuming)

-
Animal models of behaviour

-
Demonstrable results: walking robots,
pipe
-
crawling robots, military robots etc.

Reactivity


Biological inspiration:

e.g. birds flocking,

ants foraging.

Sufficiency


Grey Walter (1953) electronic tortoise.

Braitenberg (1984) synthetic psychology

Brooks (1986) behaviour
-
based robotics
and subsumption architecture.

b. Adaptivity


Adaptivity: ability to adjust oneself to the
environment


Physiological adaptation


e.g. sweating
to adjust to heat


Evolutionary adaptation


e.g. peppered
moth. Light in colour, in industrial area
became dark in colour


Sensory adaptation


e.g. our pupils
adjusting to poor light


Adaptation by learning


e.g. where food
is found


c. Situated

An emphasis on robot’s interaction
with its environment (related to
embodiment)

Brooks (1991) “the world is its own
best model”

A situated agent must respond in a
timely fashion to its inputs.


d. Embodiment


Physical grounding of robot in the world

Brooks (1991): embodiment of intelligent
systems critical because


Only an embodied intelligent agent is fully
validated as one that can deal with the
real world


Only through physical grounding can any
meaning be given to the processing
occurring within the agent

“Intelligence is determined by the
dynamics of interaction with the
world” (Brooks 1991)


-
embodied cognition

-
A solution to the symbol grounding
problem?

-
(remember Searle’s Chinese Room!)

e. Emergence


Adaptive success that emerges from
complex interactions between body,
world and brain

A non
-
centrally controlled (or
designed) behaviour that results
from the interactions of multiple
simple components

Meanings of the term
‘emergence’


Surprising situations or behaviours


Property of system not contained in
any of its parts


Behaviour resulting from agent
-
environment interaction that is not
explicitly programmed.

Ant colony


Individual ants are simple and
reactive (?)


Emergent behaviour of colony is
sophisticated

Self
-
organisation

An ant colony is self
-
organised


simple
individuals, local interactions, emergent
behaviour .. No global control


self
-
organisation is a set of dynamical mechanisms whereby
structures appear at the global level of a system from
interactions among its lower
-
level components. The rules
specifying the interactions among the system’s constituent
units are executed on the basis of purely local information,
without reference to the global pattern, which is an
emergent property of the system rather than a property
imposed upon the system by an external ordering
influence


(Bonabeau, Dorigo and Theraulaz, 1999)

Frisbee collecting robots


Robots in an arena + frisbees


Simple rules


Emergent result


clustering and
sorting of frisbees.

Changing view of intelligence



GOFAI


emphasis on reasoning,
planning, and representation.
Human
-
centred (anthropocentric)


Behaviour
-
based robotics and
beyond: emphasis on simpler
organisms and their ability to
survive in the world.

Reading:


For this week and next.

Brooks, R.A. (1991) Intelligence without Reason.
Proceedings of 1991 International Joint
Conference on Artificial Intelligence, 569
-
595.

Robots in the news


Murata Manufacturing: Murata boy


controlled by blue tooth, and can
ride a bike forwards and backwards.


9/25/2008 12:23 PM

ET


iRobot Corp. (IRBT:
News
), on Thursday, said
that it has received an additional $13.3 million
order from the US Army for PackBot 510 with
FasTac Kit robots for carrying bomb
identification and other life
-
threatening missions.