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VISIONTRAIN Thematic School
Morphological computation
Connecting brain, body and environment
Les Houches, 9-14 March 2008
Rolf Pfeifer
Artificial Intelligence Laboratory, Department of Informatics
University of Zurich, Switzerland
© Rolf Pfeifer
Lecture 2
Design principles for intelligent systems
© Rolf Pfeifer
Contents Lecture 2
• real worlds and virtual worlds
• properties of complete agents
• the quadruped „Puppy“ as a complex dynamical
system
• illustration of selected design principles
• summary
© Rolf Pfeifer
Contents Lecture 2
• real worlds and virtual worlds
• properties of complete agents
• the quadruped „Puppy“ as a complex dynamical
system
• illustration of selected design principles
• summary
© Rolf Pfeifer
Real worlds and virtual worlds
differences?
© Rolf Pfeifer
Real worlds and virtual worlds
differences?
chess vs. soccer
© Rolf Pfeifer
Real worlds and virtual worlds
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information acquisition takes time
limited information
noise
no clearly defined states
agents must do several things
own dynamics, time pressure
limited predictability, non-linear, sensitivity to
initial conditions
 bounded rationality
© Rolf Pfeifer
Contents Lecture 2
• real worlds and virtual worlds
• properties of complete agents
• the quadruped „Puppy“ as a complex dynamical
system
• illustration of selected design principles
• summary
© Rolf Pfeifer
Properties of complete agents
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•
•
•
•
subject to the laws of physics
generation sensory stimulation
affect the environment through behavior
complex dynamical systems
perform morphological computation
© Rolf Pfeifer
Contents Lecture 2
• real worlds and virtual worlds
• properties of complete agents
• the quadruped „Puppy“ as a complex dynamical
system
• illustration of selected design principles
• summary
© Rolf Pfeifer
Rapid locomotion and “cheap design”
• hard problem
© Rolf Pfeifer
Rapid locomotion
the quadruped “Puppy”
rapid locomotion
in biological systems
Design and construction:
Fumiya Iida
© Rolf Pfeifer
The quadruped “Puppy”: summary
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•
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simple control (!)
no sensors
spring-like material properties
self-stabilization
Design and construction:
Fumiya Iida
© Rolf Pfeifer
The quadruped “Puppy”: summary
•
•
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simple control (!)
no sensors
spring-like material properties
self-stabilization
principle of “cheap design”
Design and construction:
Fumiya Iida
© Rolf Pfeifer
The “mini dog” by Fumiya Iida
Artificial Intelligence Laboratory
Dept. of Information Technology
University of Zurich
© Rolf Pfeifer
The quadruped “Puppy”: summary
•
•
•
•
simple control (!)
no sensors
spring-like material properties
self-stabilization
Design and construction:
Fumiya Iida
© Rolf Pfeifer
“Puppy” on the treadmill
© Rolf Pfeifer
Video from high-speed camera –
self-stabilization
© Rolf Pfeifer
Video from high-speed camera –
self-stabilization
- no sensors
- no control
© Rolf Pfeifer
Self-stabilization
• “computation” performed by physical dynamics
of agent  basin of attraction
• stabilization through mechanical feedback
• “intra-attractor dynamics” (Kuniyoshi)
© Rolf Pfeifer
Implications of embodiment
Pfeifer et al., Science,
16 Nov. 2007)
© Rolf Pfeifer
Implications of embodiment –
self-stabilization
“Puppy”
Pfeifer et al., Science,
16 Nov. 2007)
© Rolf Pfeifer
gait patterns
100 ms
LH
Fast gallop
LF
RF
RH
LH
Moderate
walking
LF
RF
RH
LH
Fast running
trot
LF
RF
RH
0
100 200
300
400
500
600
700
800
© Rolf Pfeifer
900 1000 1100 1200 1300 1400
t (ms)
Gait patterns as attractor states
induced through interaction
with environment
Illustration by Shun Iwasawa
© Rolf Pfeifer
Morphological computation
Figure 4.1
Morphological computation. (a) Sprawl robot exploiting the material properties of its legs for rapid locomotion. The
elasticity in the linear joint provided by the air pressure system allows for automatic adaptivity of locomotion over
uneven ground, thus reducing the need for computation. (b) An animal exploiting the material properties of its legs
(the elasticity of its muscle-tendon system) thus also reducing computation. (c) A robot built from stiff materials
must
© Rolf Pfeifer
apply complex control to adjust to uneven ground and will therefore be very slow.
Gait patterns for grounding a body image
Gait 1
Gait 0
(Iida, Gomez and Pfeifer, 2005)
Fore legs:
P1i  A1i sin( i t )  B1i
Hind legs:
P2i  A2i sin( i t   i )  B2i
© Rolf Pfeifer
Contents Lecture 2
• real worlds and virtual worlds
• properties of complete agents
• the quadruped „Puppy“ as a complex dynamical
system
• illustration of selected design principles
• summary
© Rolf Pfeifer
Time-scales and design principles
Design principles
collective
intelligence
© Rolf Pfeifer
Agent design principles
Name
Description
Three constituents
Ecological niche (environment), tasks and agent must always be taken into account
Complete agents
Complete agent must be taken into account, not only isolated components
Parallel, loosely
coupled processes
Parallel, asynchronous, partly autonomous processes, largely coupled through
interaction with environment
Sensory-motor
coordination
Behavior sensory-motor coordinated with respect to target, self-generated sensory
stimulation
Cheap design
Exploitation of niche and interaction; parsimony
Redundancy
Partial overlap of functionality based on different physical processes
Ecological balance
Balance in complexity of sensory, motor, and neural systems; task dsitribution
between morphology, materials, control, and interaction with environment
Value
Driving forces: developmental mechanisms; self-organization
© Rolf Pfeifer
The Three-Constituents Principle
• ecological niche
• desired behaviors and tasks
• design of agent itself
scaffolding
© Rolf Pfeifer
Complete Agent Principle
When designing an agent, always thank about
complete agent behaving in real world.
© Rolf Pfeifer
Regonizing and
object in a
cluttered environment
manipulation of
environment facilitates
perception
robot experiments
by Giorgio Metta
illustration by
Shun Iwasawa
© Rolf Pfeifer
Regonizing and
object in a
cluttered environment
manipulation of
environment facilitates
perception
complete agent principle
principle of
information self-strcuturing
illustration by
Shun Iwasawa
© Rolf Pfeifer
Principle of “cheap design”
Exploitation of
- ecological niche
- characteristics of interaction with environment
 design easier: “cheap”
Example:
• Lecture 1: “Swiss robots”
© Rolf Pfeifer
The “Passive Dynamic Walker”
© Rolf Pfeifer
Cornell
MIT
Delft
Human
locomotion
Passive Dynamic Walker
(Cornell)
Denise
(Delft)
Qrio (Sony)
Asimo (Honda)
© Rolf Pfeifer
“Passive Dynamic Walker” – the brainless
robot (1)
Design and construction:
Ruina/Wisse/Collins, Cornell University
walking without control
Morphology:
- shape of feet
- counterswing
of arms
- friction on
bottom of feet
© Rolf Pfeifer
“Passive Dynamic Walker” – the brainless
robot (2)
Design and construction:
Ruina/Wisse/Collins, Cornell University
walking without control
Morphology:
- shape of feet
- counterswing
of arms
- friction on
bottom of feet
 self-stabilization
principle of “cheap design”
© Rolf Pfeifer
Implications of embodiment
Pfeifer et al., Science,
16 Nov. 2007)
© Rolf Pfeifer
Implications of embodiment –
self-stabilization
passive dynamic walker
Pfeifer et al., Science,
16 Nov. 2007)
© Rolf Pfeifer
Where is the memory for walking?
© Rolf Pfeifer
Extending the “Passive Dynamic Walker”
– the almost brainless robot
Design and construction:
Ruina/Wisse/Collins, Cornell University
Collins, Ruina,
Tedrake
Morphology:
- shape of feet
- counterswing
of arms
- friction on
bottom of feet
“Denise”
Martijn Wisse
© Rolf Pfeifer
Extending the “Passive Dynamic Walker”
– the almost brainless robot
Design and construction:
Martijn Wisse, Delft University
walking with little control
Morphology:
- wide feet
- counterswing of arms
- friction on bottom of
feet
- high energy efficiency
 self-stabilization
© Rolf Pfeifer
Pneuman: passive dynamic walker
(with pneumatic actuators and torso)
design and
construction:
Koh Hosoda,
Osaka
University
 self-stabilization
only hip-joint actuated
others: passive but pre-pressured (closed valves)
© Rolf Pfeifer
Implications of embodiment –
self-stabilization
Denise (Wisse)
Pneuman (Hosoda)
(Pfeifer et al., Science,
16 Nov. 2007)
© Rolf Pfeifer
Famous robots:
Asimo, Qrio, H-7, HOAP-2, HRP-2
HOAP-2 (Fujitsu)
Asimo (Honda)
HRP-2 (Kawada)
H-7 (Univ. of Tokyo)
Qrio (Sony)
© Rolf Pfeifer
Famous robots:
Asimo, Qrio, H-7, HOAP-2, HRP-2
HOAP-2 (Fujitsu)
Asimo (Honda)
HRP-2 (Kawada)
H-7 (Univ. of Tokyo)
Qrio (Sony)
© Rolf Pfeifer
Famous robots:
Asimo, Qrio, H-7, HOAP-2, HRP-2
no exploitation of dynamics, morphology,
and materials
HOAP-2 (Fujitsu)
Asimo (Honda)
HRP-2 (Kawada)
H-7 (Univ. of Tokyo)
Qrio (Sony)
© Rolf Pfeifer
Biped walking:
Exploiting interaction with environment
•
•
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•
leg as pendulum
control for free
energy efficiency
self-stabilization
principle of “cheap design”
© Rolf Pfeifer
“Cheap” diverse movement and
locomotion
© Rolf Pfeifer
Case study on morphology and materials:
“Stumpy”
almost brainless (very simple control)
two motors
actuated
joints
elastic materials
surface properties
Design and construction:
Raja Dravid, Fumiya Iida, Max Lungarella, Chandana Paul
© Rolf Pfeifer
“Cheap” behavioral diversity: “Stumpy”
Design and construction:
Raja Dravid, Fumiya Iida, Max Lungarella, Chandana Paul
© Rolf Pfeifer
“Stumpy”: Summary
• Exploitation of dynamics
– natural stiffness and elasticity of the materials
– surface properties of the feet
• many behaviors with only two joints
• self-stabilization
• good control through exploitation of
morphology and materials
 little control required
principle of “cheap design”
© Rolf Pfeifer
Implications of embodiment –
self-stabilization
Denise (Wisse)
et al., Science,
Pneuman (Hosoda) (Pfeifer
16 Nov. 2007)
Stumpy (Dravid/Iida)
© Rolf Pfeifer
Stumpy - history
design and construction:
Raja Dravid, Fumiya Iida and Chandana Paul
© Rolf Pfeifer
Exploitation of system-environment
interaction for control: ants and fish
© Rolf Pfeifer
Insect walking
Holk Cruse
• no central controller for legcoordination
• only local communication
neuronal
connections
© Rolf Pfeifer
Insect walking
Holk Cruse
• no central controller for legcoordination
• only local communication
neuronal
connections
© Rolf Pfeifer
Insect walking
Holk Cruse
• no central controller for legcoordination
• only local communication
• global communication
through interaction with
environment
neuronal
connections
© Rolf Pfeifer
Global communication through interaction with
environment
exploitation of interaction with environment
 simpler neuronal circuits
“cheap design”
“parallel, loosely
coupled processes”
angle sensors
in joints
© Rolf Pfeifer
The principle of
“parallel, loosely coupled processes”
Intelligent behavior:
• emergent from agent-environment interaction
• based on large number of parallel, loosely
coupled processes
• asynchronous
• coupled through agent’s sensory-motor system
and environment
© Rolf Pfeifer
Artificial Fish “Wanda”
© Rolf Pfeifer
Artificial Fish “Wanda”: Exploiting
morphology and systemenvironment interaction
1 DOF actuation (DOF=Degree Of Freedom)
controlling:
- up-down
- left-right
- speed
- reaching any point
in x, y, z-space
Design and construction:
Horishi Yokoi
Fumiya Iida
Mark Ziegler
© Rolf Pfeifer
Artificial Fish “Wanda”: Exploiting
morphology and systemenvironment interaction
Design and construction:
Horishi Yokoi
Fumiya Iida
Mark Ziegler
“cheap design”
© Rolf Pfeifer
Artificial Fish “Findus”: Exploiting
morphology and materials
“cheap design”
Design and construction:
Mark Ziegler
© Rolf Pfeifer
Case study: social behavior as a
collection of reflexes
© Rolf Pfeifer
Kismet - the social interaction robot
Kismet
43D.MOV
Cynthia Breazeal, MIT Media Lab
(previously MIT AI Lab)
© Rolf Pfeifer
Kismet - the social interaction robot
Kismet
Reflexes:
- turn towards loud noise
- turn towards moving objects
- follow slowly moving objects
- habituation
Cynthia Breazeal, MIT Media Lab
(previously MIT AI Lab)
© Rolf Pfeifer
Kismet - the social interaction robot Kismet
Reflexes:
- turn towards loud noise
- turn towards moving objects
- follow slowly moving objects
- habituation
Cynthia Breazeal, MIT Media Lab
(previously MIT AI Lab)
“principle of parallel, loosely
coupled processes”
© Rolf Pfeifer
Kismet - the social interaction robot Kismet
Reflexes:
- turn towards loud noise
- turn towards moving objects
- follow slowly moving objects
- habituation
Cynthia Breazeal, MIT Media Lab
(previously MIT AI Lab)
“principle of parallel, loosely
coupled processes”
social competence as a collection of reflexes ?!??
© Rolf Pfeifer
Principle of “ecological balance”
balance in complexity
• given task environment
• match in complexity of sensory, motor, and
neural system
balance / task distribution between
• morphology
• neuronal processing (nervous system)
• materials
• environment
© Rolf Pfeifer
Principle of “ecological balance”
balance in complexity
• given task environment
• match in complexity of sensory, motor, and
neural system
balance / task distribution between
• morphology
• neuronal processing (nervous system)
• materials
• environment
© Rolf Pfeifer
Snail with giant eyes
(Richard Dawkins)
ecologically unbalanced system
© Rolf Pfeifer
Braitenberg Vehicle 1 with large brain
ecologically unbalanced system
sensor for one quality
(e.g. temperature, light)
very large brain
one motor
© Rolf Pfeifer
Principle of “ecological balance”
balance in complexity
• given task environment
• match in complexity of sensory, motor, and
neural system
balance / task distribution between
• morphology
• neuronal processing (nervous system)
• materials
• environment
© Rolf Pfeifer
Examples
•
•
•
•
•
•
arm turning
loosely swinging arm
“Passive dynamic walker”
“Puppy”
“Stumpy”
cockroaches climbing over obstacles
© Rolf Pfeifer
Managing complex bodies
Brain-body cooperation
© Rolf Pfeifer
“Morphological computation” in
cockroaches
(pictures and movies
courtesy Roy Ritzmann,
Case Western Reserve Univ.)
© Rolf Pfeifer
“Morphological computation” in
cockroaches
(pictures and movies
courtesy Roy Ritzmann,
Case Western Reserve Univ.)
© Rolf Pfeifer
Self-regulating properties of coackroach
body
brain: 1 Mio. neurons
(rough estimate)
descending cells: 200 (!)
brain:
- cooperation with local circuits
- morphological changes
(shoulder joint)
Watson, Ritzmann, Zill & Pollack, 2002,
J Comp Physiol A
© Rolf Pfeifer
Changing “morphology”
brain:
1 mio neurons
(unknown)
200 descending
neurons (!)
shoulder joint
configuration
Watson, Ritzmann, Zill & Pollack, 2002, J Comp Physiol A
© Rolf Pfeifer
The redundancy principle
• redundancy prerequisite for adaptive behavior
• partial overlap of functionality in different
subsystems
• sensory systems: different physical processes
with “information overlap”
© Rolf Pfeifer
The redundancy principle
• redundancy prerequisite for adaptive behavior
• partial overlap of functionality in different
subsystems
• sensory systems: different physical processes
with “information overlap”
complementary to “cheap design”
© Rolf Pfeifer
Examples of redundancy principle
•
•
•
•
different navigation systems of ants
hands for grasping/locomotion
legs/feet for manipulation
breaking systems in airplanes
© Rolf Pfeifer
The redundancy principle
• redundancy prerequisite for adaptive behavior
• partial overlap of functionality in different
subsystems
• sensory systems: different physical processes
with “information overlap”
complementary to “cheap design”
© Rolf Pfeifer
Vision-touch
• 100 eyes
© Rolf Pfeifer
Contents Lecture 2
• real worlds and virtual worlds
• properties of complete agents
• the quadruped „Puppy“ as a complex dynamical
system
• illustration of selected design principles
• summary
© Rolf Pfeifer
Summary Lecture 2
•
•
•
•
•
intrinsic uncertainty in real world
properties of complete agents/dynamical systems
„cheap design“, self-stabilization / redundancy
parallel, loosely coupled processes
ecological balance
© Rolf Pfeifer
Agent design principles
Name
Description
Three constituents
Ecological niche (environment), tasks and agent must always be taken into account
Complete agents
Complete agent must be taken into account, not only isolated components
Parallel, loosely
coupled processes
Parallel, asynchronous, partly autonomous processes, largely coupled through
interaction with environment
Sensory-motor
coordination
Behavior sensory-motor coordinated with respect to target, self-generated sensory
stimulation
Cheap design
Exploitation of niche and interaction; parsimony
Redundancy
Partial overlap of functionality based on different physical processes
Ecological balance
Balance in complexity of sensory, motor, and neural systems; task dsitribution
between morphology, materials, control, and interaction with environment
Value
Driving forces: developmental mechanisms; self-organization
© Rolf Pfeifer
Thank you for your attention
stay tuned for lecture 3!
© Rolf Pfeifer