Bio-Inspired Solutions for
Intelligent Android
Perception and Control
Emil M. Petriu
University of Ottawa
June 2013
photo by Peter Thornton, uOttawa Gazette
In order to naturally blend within human society, the new-generation robots should
not only look as humans, but should also behave as much as possible as
humans. They are expected to be, as initially imagined by Čapek in his R.U.R. Rossum's Universal Robots play, anthropomorphic artefacts, androids, enabled to
think on their own and governed by Asimov’s laws of robotics hardwired into every robot's positronic brain.
While for a long time, engineers have built upon mathematics, physics and
chemistry in order to develop an ever growing variety of industrial artefacts
and machines, this approach cannot anymore rise to the challenge of
designing these androids.
The time has now arrived to add biology and more specifically, human
anatomy, physiology and psychology to the scientific sources of knowledge to
develop a new, bio-inspired, generation of intelligent androids.
Advocating this emergent trend, this presentation will discuss a number of relevant
issues such as bio-inspired robot sensors and neural networks, human-robot
interaction techniques for symbiotic partnership, as well as moral, ethical,
theological, legal, and social challenges in a soon-to-be cyborg-society world.
… HUMANS GETTING INTO
THE MATRIX AS AVATARS
Human
Visual
Feedback(s)
Tactile
Feedback(s)
Force
Feedback(s)
Video
Sensor(s)
Tactile
Sensor(s)
Force
Sensor(s)
Structured
Light
Audio
Feedback(s)Audio
Sensor(s)
Virtual_Environment / Real_World Interfaces
Virtual Object Manipulation
Object Shape & Behavior
ModelsObject Interaction Models
Computer Generated
Objects
Object RecognitionMotion Tracking
Sensor Data Fusion &
Interpretation
Human’s
Avatar
VIRTUAL
SCENE
AI –enabled Avatar
Animation Script The Matrix
AI-enabled
Avatar
KINECTTM
Facial Expression Recognition using a 3D
Anthropometric Muscle-Based Active Appearance Model
• Facial Action Coding System
– 7 pairs of muscles + “Jaw Drop” = Expression Space
• Muscle “contractions” control mesh deformation in “Anthropometric-Expression (AE)” space
• Texture intensities are warped into the geometry of the shape
– Shape: apply PCA in AE space
– Appearance: apply PCA in texture space
• Model defined by rigid (rotation, translation) and non-rigid motion (AE)
• Model instances synthesized from AE space,
M.D. Cordea, E.M. Petriu, D.C. Petriu, "Three-
Dimensional Head Tracking and Facial Expression
Recovery Using an Anthropometric Muscle-Based Active
Appearance Model," IEEE Trans. Instrum. Meas., vol. 57,
no. 8, pp. 1578 – 1588, 2008.
Facial Expression Recognition
• Person Dependent
• Person Independent
CyberForce®
CyberTouch™
CyberGrasp™
CyberGlove®
Immersionn_3D Interaction <http://www.immersion.com/>
A tactile human interface placed on the operator's palm allows the human
operator to virtually feel by touch the object profile measured by the tactile sensors
placed in the jaws of the robot gripper (E.M. Petriu, W.S. McMath, "Tactile Operator
Interface for Semi-autonomous Robotic Applications," Proc.Int. Symposium on Artificial Intell. Robotics Automat. in Space, i-SAIRS'92, pp.77-82, Toulouse, France, 1992.)
Tactile fingertip human interface developed
at the University of Ottawa.
It consists of miniature vibrators placed on
the fingertips. The vibrators are individually
controlled using a dynamic model of the
visco-elastic tactile sensing mechanisms in
the human fingertip.
… AI – ENABLED AVATARS
GETTING OFF THE MATRIX AS
INTELLIGENT AUTONOMOUS ANDROIDS
Matrix-trained, AI – enabled, avatar gets into the Real World as an
intelligent android able to interact and collaborate with humans
Virtual Object Manipulation
Object Shape & Behavior
ModelsObject Interaction Models
Computer Generated
Objects
Object RecognitionMotion Tracking
Sensor Data Fusion &
Interpretation
AI-enabled
Avatar
VIRTUAL
SCENE
AI-enabled Avatar
Animation Script The Matrix
Intelligent
Android
Human’s
Avatar
Real World
Human
“The idea of the uncanny valley was
proposed by Masahiro Mori in 1970. His
idea was that increasing humanness in a
robot was positive only up to a certain
point …. beyond which, the not-quite-
human object strikes people as creepy.”
Crossing the uncanny valley: As computer graphics and
robots get more human, they often seem more surreal [The Economist, Nov 18th 2010 , http://www.economist.com/node/17519716 ]
“InMoov”, the first Open Source life size humanoid robot you
can 3D print and animate, Gael Langevin’s project, January 2012
http://www.inmoov.fr/project/ ,
https://www.youtube.com/watch?v=vXJhgvVal7Y
“Gael Langevin is a French modelmaker and sculptor. He works for the biggest brands since
more than 25 years. InMoov is his personal project, it was initiated in January 2012
InMoov is the first Open Source 3D printed life-size robot. Replicable on any home 3D printer
with a 12x12x12cm area, it is conceived as a development platform for Universities,
Laboratories, Hobbyist, but first of all for Makers. It’s concept, based on sharing and community,
gives him the honor to be reproduced for countless projects through out the world.”
For many centuries, engineers were building upon mathematics and
natural science principles from mechanics, electricity, and chemistry
in order to develop an ever growing variety of more efficient and smarter
industrial artefacts and machines.
The time has now arrived to
add biology - and more
specifically, human anatomy,
physiology and psychology –
to the scientific sources of
knowledge for engineers to
develop a new generation of
bio-inspired intelligent machines.
Model of the real world perceived by the human brain through sensory organs
Real/Material World
Biology-Inspired Robot Perception &
Action Mechanisms for Androids
The sensory cortex: an oblique strip, on
the side of each hemisphere, receives
sensations from parts on the opposite side
of the body and head: foot (A), leg (B, C,
hip (D), trunk (E), shoulder (F), arm (G, H),
hand (I, J, K, L, M, N), neck (O), cranium
(P), eye (Q), temple (R), lips (S), cheek (T),
tongue (U), and larynx (V). Highly sensitive
parts of the body, such as the hand, lips,
and tongue have proportionally large
mapping areas, the foot, leg, hip, shoulder,
arm, eye, cheek, and larynx have
intermediate sized mapping areas, while
the trunk, neck, cranium, and temple have
smaller mapping areas.
(from [H. Chandler Elliott, The Shape of Intelligence - The Evolution of the Human Brain, Drawings by A. Ravielli, Charles
Scribner’s Sons, NY, 1969])
Bio-Inspired Sensing & Perception
Artificial Neural Networks
Biological Neurons
Neurons are rather slow (10-3 s) when compared
with the modern electronic circuits. ==> The brain is
faster than an electronic computer because of its
massively parallel structure. The brain has
approximately 1011 highly connected neurons
(approx. 104 connections per neuron).
Dendrites carry electrical signals in into the neuron
body. The neuron body integrates and thresholds the
incoming signals.The axon is a single long nerve
fiber that carries the signal from the neuron body to
other neurons. A synapse is the connection between
dendrites of two neurons.
Memories are formed by the modification of the
synaptic strengths which can change during the
entire life of the neural systems.
Body
Axon
Dendrites
Synapse
Looking for a model to prove that
algebraic operations with analog
variables can be performed by logic
gates, Professor J. von Neuman
advanced in 1956 the idea of representing analog variables by the mean rate of random-pulse streams [J. von Neuman, “Probabilistic logics and
the synthesis of reliable organisms
from unreliable components,” in Automata Studies, (C.E. Shannon, Ed.), Princeton, NJ, Princeton University Press, 1956].
FS+VFS-V
FS FSXQ
p.d.f.
of VR
1
2 FS.
-FS
+FS
1
V
X
0
-1
VRQ1-BIT QUANTIZER
X-FS
+FS
XQ
X
0
1
-1
XQ
CLOCKCLK
VRP
ANALOG RANDOM
SIGNAL GENERATOR
-FS +FS0
R
p(R)1
2 FS
+
+VR
V
R
Analog/Random-Pulse Conversion
Analog/Random-Pulse and Random-Pulse/Digital Conversion
• E.M. Petriu, K. Watanabe, T. Yeap, "Applications of Random-Pulse Machine Concept to Neural Network
Design," IEEE Trans. Instrum. Meas., Vol. 45, No.2, pp.665-669, 1996,
• E. Pop, E.M. Petriu, "Influence of Reference Domain Instability Upon the Precision of Random Reference
Quantizer with Uniformly Distributed Auxiliary Source," Signal Processing (EURASIP), North Holland, Vol. 5,
pp.87-96, 1983
Generalized b-bit analog/random-data conversion
VR
V
RVRQ
CLOCKCLK
VRP
b -BIT
QUANTIZER
X XQ
ANALOG RANDOM
SIGNAL
GENERATOR
-D/20
R
p(R)
1/D
+D/2
+
+
.(k+0.5)D(k-0.5)D.
XQXQXQXQ
XXXX
k
k+1
k-1
0
b D.
1/ Dp.d.f.of VR
D /2D /2
b D. (1-b) D.
.V= (k-b) Dk D.
E.M. Petriu, L. Zhao, S.R. Das, V.Z. Groza, A. Cornell, “Instrumentation Applications of Multibit
Random-Data Representation,” IEEE Trans. Instrum. Meas., Vol. 52, No. 1, pp. 175- 181, 2003.
Stochastic Data Representation
Quantizat
ion
levels
Relative mean
square error
2 72.23
3 5.75
4 2.75
... ...
8 1.23
... ...
analog 10 10 20 30 40 50 60 70
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Moving average window size
Mean s
quare
err
or
1-Bit
2-Bit
Neural Network Architectures Using
Stochastic Data Representation
ΣΣΣΣF
Y = F [ w X ]Σj=1
m.j iij
SY
NA
PS
E
SY
NA
PS
E
SY
NA
PS
E
. . .. . . X mX 1 X i
wmj
wij
w1j
Auto-associative memory NN architecture
P1, t1 P2, t2 P3, t3
Training setRecovery of 30%
occluded patterns
30
P
30x1
30x30
n
30x1
a
30x1W
)*Hardlim ( P Wa =
Neural Network for Pattern Recognition
Neural Network vs.
Analog Computer Modelling
Both the Analog Computers and Neural Networks
are continuous modelling devices.
Neural Networks don’t require a prior mathematical
models. A learning algorithm is used to adjust by trial
and error during the learning phase the synaptic
weights of the neurons.
Discreet vs. Continuous Modelling of
Physical Objects and Processes
x
y
CONTINUOUS MODEL
• NO sampling => NO INTEPPOLATION COST
DISCREET MODEL
• sampling => INTERPOLATION COST y(j) = y(A) +
[ x(j)-x(B)] . [ y(B)-x(A)] / [x(A)-x(B)]
x
y
x(j)
y(j) = ?
A
B
Compare the performance of three NN architectures used for 3D
object shape modelling:
• Multilayer Feedforward (MLFF ) • Self-Organizing Map (SOM ) • Neural Gas Network
A.-M. Cretu, E.M. Petriu, G.G. Patry, “Neural-Network-Based Models of 3-D Objects for Virtualized Reality: A Comparative Study,” IEEE Trans. Instrum. Meas.," Vol. 55, No. 1, pp.99-111, 2006.
NN Modelling of 3D Object Shapes
51096 points, 20-10-1,5 extra surfaces,d=0.055, 2000 epochs,5.2 hrs.
2500 points, 12-6-1,2 extra surfaces, d=0.06,1020 epochs,45 min.
MLFF Representation - Results19000 points, 14-7-1,4 extra surfaces,d=0.055, 1100 epochs,3.3 hrs
Sampling points selected with
the neural gas network for the ball.Elastic ball used
for experimentation.
(from A.M. Cretu, E.M. Petriu, P.Payeur “Neural Network Mapping and Clustering of Elastic Behavior from Tactile and
Range Imaging for Virtualized Reality Applications,” submitted to IEEE Tr. Instr. Meas., Nov. 2006 ).
(a) (b)
Real and modeled deformation curves using neural network for rubber under
forces applied at different angles:
a) F=65N, α1=10° and F=65N, α2=170°,
b) F=36N, α1=25°, and F=36N, α2=155
(from .A.M. Cretu, E.M. Petriu, P.Payeur “Neural Network Mapping and Clustering of Elastic Behavior from Tactile and
Range Imaging for Virtualized Reality Applications,” submitted to IEEE Tr. Instr. Meas., Nov. 2006).
Pioneered by Zadeh in the mid ‘60s fuzzy logic provides
the formalism for modeling the approximate reasoning mechanisms specific to the human brain.
“In more specific terms, what is central about fuzzy
logic is that, unlike classical logical systems, it aims at
modeling the imprecise modes of reasoning that play an
essential role in the remarkable human ability to make
rational decisions in an environment of uncertainty and
imprecision. This ability depends, in turn, on our ability
to infer an approximate answer to a question based on a
store of knowledge that is inexact, incomplete, or not
totally reliable.” [ “Fuzzy Logic,” IEEE Computer Mag,April 1988, pp. 83-93: ]
FUZZY LOGIC
ANALOG (CRISP)
-TO-FUZZY
INTERFACE
FUZZIFICATION
FUZZY-TO-
ANALOG (CRISP)
INTERFACE
DEFUZZIFICATION
SENSORS ACTUATORS
INFERENCE
MECHANISM
(RULE EVALUATION)
FUZZY RULE BASE
PROCESSThe basic idea of “fuzzy logic control”
(FLC) was suggested by L.A. Zadeh, “A
rationale for fuzzy control,” J. Dynamic Syst. Meas. Control, vol.94, series G,
pp.3-4,1972.
FLC provides a non analytic alternative
to the classical analytic control theory.
==> “But what is striking is that its
most important and visible
application today is in a realm not
anticipated when fuzzy logic was
conceived, namely, the realm of fuzzy-
logic-based process control,” [L.A.
Zadeh, “Fuzzy logic,” IEEE Computer Mag., pp. 83-93, Apr. 1988].
Early FLCs were reported
by Mamdani and Assilian in
1974, and Sugeno in 1985.
CONTROLLED
SYSTEM
DESIRED SYSTEM FUNCTION
INPUT
OUTPUT
x*
y*
Classic control is based on a detailed I/O
function OUTPUT= F (INPUT) which maps
each high-resolution quantization interval of
the input domain into a high-resolution
quantization interval of the output domain.
=> Finding a mathematical expression for
this detailed mapping relationship F may be
difficult, if not impossible, in many applications.
INPUT
OUTPUT
Fuzzification
y*
x*
Defu
zzific
ation
Fuzzy logic control is based on an I/O function that maps
each very low-resolution quantization interval of the input
domain into a very low-low resolution quantization interval
of the output domain. As there are only 7 or 9 fuzzy
quantization intervals covering the input and output domains
the mapping relationship can be very easily expressed using
the“if-then” formalism. (In many applications, this leads to a
simpler solution in less design time.) The overlapping of these
fuzzy domains and their linear membership functions will
eventually allow to achieve a rather high-resolution I/O
function between crisp input and output variables.
Fuzzy Logic Control
The key benefit of FLC is that the desired system behavior can be
described with simple “if-then” relations based on very low-
resolution models able to incorporate empirical engineering
knowledge. FLCs have found many practical applications in the
context of complex ill-defined processes that can be controlled
by skilled human operators: water quality control, automatic train
operation control, elevator control, etc.,
There is tenet of common wisdom that FLCs are meant to successfully
deal with uncertain data. According to this, FLCs are supposed to
make do with “uncertain” data coming from (cheap) low-resolution and
imprecise sensors.
However, using a truck backing-up Fuzzy Logic Controller (FLC) as test
bed, experiments show that the low resolution of the sensor data
results in rough quantization of the controller's I/O characteristic:
Experiments have shown also show that it is possible to smooth the
I/O characteristic of a digital FLC by dithering the sensor data before
quantization
“FUZZY UNCERTAINTY” –
WHAT ACTUALLY IS “FUZZY” IN A FUZZY CONTROLLER ??
E.M. Petriu, J. Mao, "Fuzzy Sensing and Control for a Truck," Proc. VIMS-2000, IEEE Workshop on Virtual and Intelligent Measurement Systems, pp. 27-32, Annapolis, MD,
April 2000.
ϕθ
d
Loading Dock
( , )x y Front Wheel
Back Wheel
(0,0)x
y
The truck backing-up
Design a Fuzzy Logic
Controller (FLC) able to back
up a truck into a docking
station from any initial position
that has enough clearance
from the docking station.
0 4 5 15 20-4-5-15-20-5050
x-position
900 100080060030000-90 27001200 1500 1800
truck angle
00-250-350-450 250 350 450
LE LC CE RC RI
RB RU RV VE LV LU LB
NL NM NS ZE PS PM PL
ϕ
steering angle θ
0.0
1.0
1.0
0.0
Membership
functions for the
truck backer-
upper FLC
PS
NS
NM
NM
NL
NL
NL
PM PM
PM
PL PL
NL
NL
NM
NM
NS
PS
NM
NM
NS
PS
NM
NS
PS
PM
PM
PL
NS
PS
PM
PM
PL
PL
RL
RU
RV
VE
LV
LU
LL
LE LC CE RC RI
ϕ
x
ZE
1 2 3 4 5
6 7
18
31 35343332
30
The FLC is based
on the Sugeno-style
fuzzy inference.
The fuzzy rule base
consists of 35 rules.
0 10 20 30 40 50 60 70-40
-30
-20
-10
0
10
20
30
Time (s)
θ [deg]
0 10 20 30 40 50 60-50
-40
-30
-20
-10
0
10
20
30
40
Time (s)
θ [deg]
Time diagram of digital FLC's output q during a docking
experiment when the input variables, j and x are analog and
respectively quantized with a 4-bit bit resolution
A/D
A/D
∑
Dither
∑
Dither
Low-Pass
Filter
Low-Pass
Filter
Digital
FLC
Analog
Input
Analog
Input
Dithered
Analog Input
High Resolution
Digital Output
Low-Resolution
Dithered Digital
Input
High Resolution
Digital Output
Low-Resolution
Dithered Digital
Input
Dithered
Analog Input
Dithered digital FLC architecture with low-pass filters placed at the FLC's
outputs to smooth the non-linearity caused by the min-max composition
rules of the FLC.
0 10 20 30 40 50 60 70-40
-30
-20
-10
0
10
20
30
Time (s)
θ [deg]
0 10 20 30 40 50 60-50
-40
-30
-20
-10
0
10
20
30
40
Time (s)
θ [deg]
Time diagram of digital FLC's output q
during a docking experiment when the
input variables, j and x are: (upper left) analog, (upper right) quantized with a
4-bit bit resolution, and (left) dithered
before being 4-bit bit quantized and
then a low-pass filter is placed at the
FLC's output
0 10 20 30 40 50 60 70-50
-40
-30
-20
-10
0
10
20
30Θ [deg]
Time (s)
-50 50
0
10
20
30
40
50
X
Y
(a)
(b)
(c)
[dock]
initial position
(-30,25)
0
Truck trails for different FLC architectures: (a) analog ; (b)
digital without dithering; (c) digital with uniform dithering and
20-unit moving average filter
Dithered FLCDigital FLC
Analog FLC
The skin of a human finger contains
four types of cutaneous sensing
elements distributed within the skin:
Meissner’s corpuscles for sensing
velocity and movement across the
skin; Merkel’s disks for sensing
sustained pressure and shapes;
Pacinian corpuscles for sensing
pressure changes and vibrations of
about 250 Hz; and Ruffini corpusclesfor sensing skin stretch and slip.
(from R. Sekuler and R. Balke,
Perception, McGraw-Hill, 1990)
BIO-INSPIRED ROBOT SENSING AND ACTUATION
Human Tactile Sensing
Robot arm with tendon driven
compliant joint
(E.M. Petriu, D.C. Petriu, V. Cretu, "Control
System for an Interactive Programmable Robot,"
Proc. CNETAC Nat. Conf. Electronics, Telecommunications, Control, and Computers, pp.
227-235, Bucharest, Nov. 1982, and E.M. Petriu,
D. Petriu, V. Cretu, "Multi-Microprocessor Control
System for an Experimental Robot with Elastic
Joints," Proc. Nat. Conf. Cybernetics, (in
Romanian), Bucharest, Romania, 1981).
The tabs of the elastic overlay
are arranged in a 16-by-16 array
having a tab on top of each node
of Merkel’s disk-like matrix of
FSR elements sensing sustained
pressure and shapes.
This tab configuration provides a
de facto spatial sampling, which
reduces the elastic overlay's
blurring effect on the high 2D
sampling resolution of the FSR sensing matrix.
• P. Payeur, C. Pasca, A.-M.Cretu, E.M. Petriu, “Intelligent Haptic Sensor System for Robotic Manipulation,”
IEEE Trans. Instrum. Meas., Vol. 54, No. 4, pp. 1583 – 1592, 2005,
• W.S. McMath, S.K.S. Yeung, E.M. Petriu, "Tactile Sensing for Space Robotics," Proc. IMTC/89, IEEE Instrum. Meas. Technol. Conf., pp.128-131, Washington, DC., 1989.
Tactile Sensor
Example of GUI window (from [C. Pasca, Smart Tactile Sensor,M.A.Sc. Thesis, University of Ottawa, 2004])
Bio-inspired robot passive-compliant wrist allowing the tactile probe to
accommodate the constraints of the touched object surface and thus to increase
the local cutaneous information extracted during the active exploration process
under the force provided by the robot.
Feeling the temperature and thermal conductivityof the touched object surface. Rufini corpuscles-like
thermistors and a blood-vessel like source of heat (the white
coloured tube) distributed within the tactile sensor’s elastic skin.
3D generic face deformed using muscle-based control
Avatar Face
Neutral Happy
Sad Surprised
Combining muscle
actions it becomes
possible to obtain a
variety of facial
expressions of
Marius’ avatar:
M.D. Cordea, E.M. Petriu, “A 3-D Anthropometric-Muscle-Based Active Appearance Model,” IEEE Trans. Instrum. Meas., Vol. 55, No. 1, pp. 91 - 98, 2006.
• Plastic skull
• Latex rubber
• Proof-of-concept design
Android Face
P. Santos, E. de Castro Maia Jr., M, Goubran, E.M. Petriu, “Facial Expression
Communication for Healthcare Androids,” Proc. MeMeA2013, 8th IEEE Int. Symp. on Medical Measurement and Applications, pp. 44-48, Ottawa, ON, Canada, May 2013
P. Santos, E. de Castro Maia Jr., M, Goubran, E.M. Petriu, “Facial Expression
Communication for Healthcare Androids,” Proc. MeMeA2013, 8th IEEE Int. Symp. on Medical Measurement and Applications, pp. 44-48, Ottawa, ON, Canada, May 2013
Avatar-Android Face Expressions Mapping
From left to right: neutral, happiness, sadness, surprise, anger, fear, disgust
P. Santos, E. de Castro Maia Jr., M, Goubran, E.M. Petriu, “Facial Expression Communication for Healthcare
Androids,” Proc. MeMeA2013, 8th IEEE Int. Symp. on Medical Measurement and Applications, pp. 44-48,
Ottawa, ON, Canada, May 2013
Face and Lip Animation Using Model-based Audio and Video Coding
M. D. Bondy, E. M. Petriu, M. D. Cordea, N. D. Georganas, D. C. Petriu, T. E. Whalen, “Model-based Face and Lip
Animation for Interactive Virtual Reality Applications”, Proc. ACM Multimedia 2001, pp. 559-563, Ottawa, ON, Sept. 2001
The parameters of the lip contour model
xo, yo = the origin of the outside parabolas;
xi, yi = the origin of the inside parabolas; Bo= outer height; Bi = inner height; Ao = outer
width; Ai = inner width; D = depth of ‘dip’;
C = width of ‘dip’; E = offset height of
cosine function; tordero = top outside
parabola order; bordero = bottom outside
parabola order; orderi =inside parabola order
(same on both top an bottom).
The lip contur model used in the mapping:
The only parameters of the lip model that are associated to the cepstral coefficients are the
outer width Ao and the outer height Bo. Relations can be found linking the parameter values
of the inner contour of the lip model to the parameter values of the outer contour.
Therefore, estimating the inner contour values from the audio signal would be redundant.
Examples of the lip
model being molded
to the shape of the
speaker lips
Comparing the speech-
driven and the real lip shape
for a female speaker
saying in French the ten
digits: zero, un, deux,...neuf.
M. D. Bondy, E. M. Petriu, M. D. Cordea, N. D. Georganas, D. C. Petriu, T. E. Whalen, “Model-based Face and Lip
Animation for Interactive Virtual Reality Applications”, Proc. ACM Multimedia 2001, pp. 559-563, Ottawa, ON, Sept. 2001
Behaviour-Based
Android Control
ANIMATION
SCRIPT
Voice
synthesizer
Face
muscle-
activation
instructions
Joint-
activation
instructions
Face Model
(Facial Action Coding )
Body Model
(Joint Control )
3-D ARTICULATED ANDROID MODEL
Android /“Machine” – level Instructions
Story-level
Instructions
INTERPRETER/COMPILER
INVERSE KINEMATIC CONTROL
STORY-LEVEL INSTRUCTIONS
…..
DaneelA sits on the chair#4.
DanielA writes “Hello” on stationary.
He sees HappyCat under the white table .
DaneelA starts smiling.
HappyCat grins back.
……
BEHAVIOUR-LEVEL (“MACRO”) INSTRUCTIONS
…..
DanielA’s right hand moves the pen to follow the trace representing “H”.
DanielA’s right hand moves the pen to follow the trace representing “e”.
DanielA’s right hand moves the pen to follow the trace representing “l”.
DanielA’s right hand moves the pen to follow the trace representing “l”.
DanielA’s right hand moves the pen to follow the trace representing “o”.
……
DanielA’s specific style
of moving his right arm
joints to write “H”
( NN model capturingDanielA’s writing personality )
Rotate Wrist to a i
Rotate Elbow to b j
Rotate Shoulder to g k
Wrist
Elbow
Shoulder
x
y
z
3-D Model of DanieA’s Right Hand
BEHAVIOUR-LEVEL (“MACRO”) INSTRUCTIONS
…
DanielA’s right hand moves the pen to follow the
trace representing “H”.
…
Human & Android & Cyborg
Hyper-Society
TECHNOLOGICALLY ENHNCED HUMAN - CYBORG
eye glasses, binoculars, IR night
vision, HMD for augmented VR,...
gloves (baseball glove), hand tools
footwear, skates, bike, exoskeleton,..Knee Joint +
Artificial Knee Joint
Ear +Hearing Aid
Implant
Eye +Artificial Cornea
HUMAN
Heart +Pacemaker
Hand +Artificial Hand
Nose +Artificial Smell
Tongue +Artificial Taste
Brain Prosthesis
Brain Prosthesis which learns/models with an ever increasing fidelity the behaviour of
the natural brain so it can be used as behavioural-memory prosthesis (BMP) to make
up for the loss in the natural brain’s functions due to dementia, Alzheimer disease, etc.
It is quite conceivable that such a BMP could arrive in extremis to complete replace the
functions of the natural brain.
“Immortality by 2045 or bust: Russian tycoon
wants to transfer minds to machines
Russian billionaire Dmitry Itskov speaks to the Global
Future 2045 Congress, Saturday, June 15, 2013 at
Lincoln Center in New York. Some of humanity’s best
brains are gathering in New York to discuss how our
minds can outlive our bodies.” [Ottawa Citizen, June
15, 2013, http://www.ottawacitizen.com/business/Immortality+2045+bust+
Russian+tycoon+wants+transfer+minds/8531949/story.html]
Cyber/Machine
Society/World
{Intelligent Androids}
Human
Society/World
{Human Beings}
Asimov’s laws of the
robotics:
1st law: “A robot must notharm a human being or,through inaction allow oneto come to harm”.
2nd law: “A robot must
always obey human beings
unless that is in conflict with
the 1st law”.
3rd law: “A robot must
protect itself from harm
unless that is in conflict with
the 1st and 2nd law”.
Asimov’s laws of the robotics:
0th law: "A robot may not injure humanity or, through inaction, allow humanity to come to harm."
1st law- updated: “A robot must not harm a human being or, through inaction allow one to come to harm, unless this would violate the 0th law."
2nd law: “A robot must always
obey human beings unless that is
in conflict with the 1st law”.
3rd law: “A robot must protect itself
from harm unless that is in conflict
with the 1st and 2nd law”.
__________
[*] I. Asimov, Robots and Empire, Doubleday & Co., NY 1985, p.291
Cyber/Machine
Society/World
{Intelligent Androis}
Human
Society/World
{Human Beings}
Cyborg
Society/
World
{Cyborgs}
Moral, Ethical,
Theological, Legal, Biological,
Psychological Social,
Economic Challenges
Thank You!