Bio-Inspired Solutions for Intelligent Android Perception and Control. Emil M. Petriu University of Ottawa

Transcription

1 Bio-Inspired Solutions for Intelligent Android Perception and Control Emil M. Petriu University of Ottawa June 2013

2 photo by Peter Thornton, uottawa Gazette

3 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.

4 HUMANS GETTING INTO THE MATRIX AS AVATARS

5 Computer Generated Objects Object Interaction Models AI enabled Avatar Animation Script Object Shape & Behavior Models The Matrix Sensor Data Fusion & Interpretation Virtual Object Manipulation Motion Tracking Object Recognition AI-enabled Avatar VIRTUAL SCENE Virtual_Environment / Real_World Interfaces Visual Feedback(s) Audio Feedback(s) Video Sensor(s) Structured Light Audio Sensor(s) Human s Avatar Tactile Feedback(s) Force Feedback(s) Human Tactile Sensor(s) Force Sensor(s)

6 KINECT TM

7 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 , 2008.

8 Person Dependent Facial Expression Recognition Person Independent

9 Immersionn_3D Interaction < CyberGlove CyberTouch CyberGrasp CyberForce

10 A tactile human interfaceplaced 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.)

11 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.

12 AI ENABLED AVATARS GETTING OFF THE MATRIX AS INTELLIGENT AUTONOMOUS ANDROIDS

13 Computer Generated Objects Object Interaction Models AI-enabled Avatar Animation Script Object Shape & Behavior Models The Matrix Sensor Data Fusion & Interpretation Virtual Object Manipulation Motion Tracking Object Recognition Human s Avatar VIRTUAL SCENE Real World Human AI-enabled Avatar Intelligent Android Matrix-trained, AI enabled, avatar gets into the Real World as an intelligent android able to interact and collaborate with humans

14 Crossing the uncanny valley: As computer graphics and robots get more human, they often seem more surreal [The Economist, Nov 18th 2010, The idea of the uncanny valleywas proposed by Masahiro Mori in His idea was that increasing humanness in a robot was positive only up to a certain point. beyond which, the not-quitehuman object strikes people as creepy.

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16 InMoov, the first Open Source life size humanoid robot you can 3D print and animate, Gael Langevin s project, January 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.

17 For many centuries, engineers were building upon mathematicsand 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 knowledgefor engineers to develop a new generation of bio-inspired intelligent machines.

18 Biology-Inspired Robot Perception & Action Mechanisms for Androids Model of the real world perceived by the human brain through sensory organs Real/Material World

19 Bio-Inspired Sensing & Perception 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])

20 Artificial Neural Networks

21 Biological Neurons Body Axon Dendrites Synapse Dendritescarry electrical signals in into the neuron body. The neuron bodyintegrates and thresholds the incoming signals.the axonis a single long nerve fiber that carries the signal from the neuron body to other neurons. A synapseis the connection between dendrites of two neurons. Memoriesare formed by the modification of the synaptic strengthswhich can change during the entire life of the neural systems. 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 highly connected neurons (approx connections per neuron).

22 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].

23 Analog/Random-Pulse Conversion V ANALOG RANDOM SIGNAL GENERATOR p(r) 1 2 FS -FS 0 + R +FS + R VR p.d.f. of VR XQ X -FS 1 XQ X 0-1 +FS 1-BIT QUANTIZER VRQ CLK CLOCK XQ FS FS 1 VRP 2. FS FS-V FS+V -FS 1 X 0-1 V +FS

24 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 , 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

25 Stochastic Data Representation XQ D /2 D /2 V + + R VR ANALOG RANDOM SIGNAL GENERATOR p(r) 1/D -D/20 R +D/2 X b-bit XQ QUANTIZER CLOCK CLK VRQ VRP k+1 k k-1 0 p.d.f. of VR b. D (1-b). D b. D (k-0.5)d. k. D V= (k-b). D 1/ D. (k+0.5)d X Generalized b-bit analog/random-data conversion 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 , 2003.

26 Quantizat ion levels Relative mean square error Mean square error Bit analog Bit Moving average window size

27 Neural Network Architectures Using Stochastic Data Representation X 1... Xi... X m SYNAPSE w 1j SYNAPSE w ij SYNAPSE w mj Σ F m Y j= F [ Σ w ij. X i ] j=1

28 Neural Network for Pattern Recognition Auto-associative memory NN architecture a P 30x1 W 30x30 n 30x1 30x1 30 a = Hardlim ( W * P ) P 1, t 1 P 2, t 2 P 3, t 3 Training set Recovery of 30% occluded patterns

29 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 algorithmis used to adjust by trial and error during the learning phase the synaptic weights of the neurons.

30 Discreet vs. Continuous Modelling of Physical Objects and Processes y B A y y(j) =? x(j) x x DISCREET MODEL sampling => INTERPOLATION COST y(j) = y(a) + [ x(j)-x(b)]. [ y(b)-x(a)] / [x(a)-x(b)] CONTINUOUS MODEL NO sampling=> NO INTEPPOLATION COST

31 NN Modelling of 3D Object Shapes 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 , 2006.

32 MLFF Representation - Results points, , 4 extra surfaces, d=0.055, 1100 epochs, 3.3 hrs points, , 5 extra surfaces, d=0.055, 2000 epochs, 5.2 hrs points, , 2 extra surfaces, d=0.06, 1020 epochs, 45 min.

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34 Elastic ball used for experimentation. Sampling points selected with the neural gas network for the ball. (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 ).

35 (a) (b) Real and modeled deformation curves using neural network for rubber under forces applied at different angles: a) F=65N, α 1 =10 and F=65N, α 2 =170, b) F=36N, α 1 =25, and F=36N, α 2 =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).

36 FUZZY LOGIC 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 : ]

37 The 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. SENSORS CONTROLLED SYSTEM PROCESS ACTUATORS 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 fuzzylogic-based process control, [L.A. Zadeh, Fuzzy logic, IEEE Computer Mag., pp , Apr. 1988]. Early FLCs were reported by Mamdani and Assilian in 1974, and Sugeno in ANALOG (CRISP) -TO-FUZZY INTERFACE FUZZIFICATION FUZZY-TO- ANALOG (CRISP) INTERFACE INFERENCE MECHANISM (RULE EVALUATION) FUZZY RULE BASE DESIRED SYSTEM FUNCTION DEFUZZIFICATION

38 Fuzzy Logic Control OUTPUT OUTPUT y* y* Defuzzification x* INPUT Fuzzification INPUT x* Classic controlis 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. 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.

39 The key benefit of FLCis that the desired system behavior can be described with simple if-then relations based on very lowresolution 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.,

40 FUZZY UNCERTAINTY WHAT ACTUALLY IS FUZZY IN A FUZZY CONTROLLER?? 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 characteristicof a digital FLC byditheringthe sensor data before quantization E.M. Petriu, J. Mao, "Fuzzy Sensing and Control for a Truck," Proc. VIMS-2000, IEEE Workshop on Virtual and Intelligent Measurement Systems, pp , Annapolis, MD, April 2000.

41 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. ( x, y) ϕ θ Front Wheel Back Wheel d (0,0) Loading Dock y x

42 1.0 LE LC CE RC RI x-position 1.0 RB RU RV VE LV LU LB truck angle ϕ Membership functions for the truck backerupper FLC NL NM NS ZE PS PM PL steering angle θ

43 ϕ x RL LE LC CE RC RI NL NL NM NM NS RU NL 6 7 NL NM NS PS RV NL NM NS PS PM The FLC is based on the Sugeno-style fuzzy inference. The fuzzy rule base consists of 35 rules. VE LV LU LL NM NM NS PS NM NS ZE PS PM PL PL PM PM 18 PM PM PS PM PL PL PL

44 30 20 θ [deg] θ [deg] Time (s) Time (s) 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

45 Dither Analog Input Dithered Analog Input A/D Low-Resolution Dithered Digital Low-Pass Filter High Resolution Digital Output Analog Input Dither Dithered Analog Input A/D Low-Resolution Dithered Digital Digital FLC Low-Pass Filter High Resolution Digital Output 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.

46 θ [deg] θ [deg] Time (s) Time (s) Θ [deg] Time (s) 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

47 Y initial position (-30,25) (a) (c) 10 (b) 0-50 [dock] 0 50 X Truck trails for different FLC architectures: (a) analog ; (b) digital without dithering; (c) digital with uniform dithering and 20-unit moving average filter

48 Analog FLC Digital FLC Dithered FLC

49 BIO-INSPIRED ROBOT SENSING AND ACTUATION Human Tactile Sensing The skin of a human finger contains four types of cutaneous sensing elementsdistributed within the skin: Meissner s corpusclesfor sensing velocity and movement across the skin; Merkel s disksfor sensing sustained pressure and shapes; Pacinian corpusclesfor sensing pressure changes and vibrations of about 250 Hz; and Ruffini corpuscles for sensing skin stretch and slip. (from R. Sekuler and R. Balke, Perception, McGraw-Hill, 1990)

50 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 , 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).

51 Tactile Sensor 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 factospatial 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 , 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 , Washington, DC., 1989.

52 Example of GUI window(from [C. Pasca, Smart Tactile Sensor, M.A.Sc. Thesis, University of Ottawa, 2004])

53 Bio-inspiredrobot 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.

54 Feeling the temperature and thermal conductivity of 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.

55 Avatar Face 3D generic face deformed using muscle-based control

56 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 , 2006.

57 Android Face Plastic skull Latex rubber Proof-of-concept design 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 , Ottawa, ON, Canada, May 2013

58 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 , Ottawa, ON, Canada, May 2013

59 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 , Ottawa, ON, Canada, May 2013

60 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 , Ottawa, ON, Sept. 2001

61 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 A o and the outer height B o. 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.

62 Examples of the lip model being molded to the shape of the speaker lips Comparing the speechdriven 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 , Ottawa, ON, Sept. 2001

63 Behaviour-Based Android Control

64 Android / Machine level Instructions INTERPRETER/COMPILER INVERSE KINEMATIC CONTROL 3-D ARTICULATED ANDROID MODEL Face Model (Facial Action Coding ) Body Model (Joint Control ) Face muscleactivation instructions Jointactivation instructions Story-level Instructions Voice synthesizer ANIMATION SCRIPT

65 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.

66 BEHAVIOUR-LEVEL ( MACRO ) INSTRUCTIONS DanielA s right hand moves the pen to follow the trace representing H. DanielA s specific style of moving his right arm joints to write H ( NN model capturing DanielA s writing personality ) Rotate Wrist to a i Rotate Elbow to b j Rotate Shoulder to g k 3-D Model of DanieA s Right Hand Shoulder z Elbow y x Wrist

67 Human & Android & Cyborg Hyper-Society

68 Heart + Pacemaker Eye + Artificial Cornea eye glasses, binoculars, IR night vision, HMD for augmented VR,... HUMAN Ear + Hearing Aid Implant Nose + Artificial Smell Tongue + Artificial Taste Hand + Artificial Hand gloves (baseball glove), hand tools Knee Joint + Artificial Knee Joint footwear, skates, bike, exoskeleton,.. TECHNOLOGICALLY ENHNCED HUMAN - CYBORG

69 Brain Prosthesis 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, Russian+tycoon+wants+transfer+minds/ /story.html] Brain Prosthesiswhich 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.

70 Asimov s laws of the robotics: 1 st law: A robot must not harm a human being or, through inaction allow one tocometoharm. 2 nd law: A robot must always obey human beings unless that is in conflict with the 1 st law. 3 rd law: A robot must protect itself from harm unless that is in conflict with the 1 st and 2 nd law. Cyber/Machine Society/World {Intelligent Androids} Human Society/World {Human Beings}

71 Asimov s laws of the robotics: 0 th law: "A robot may not injure humanity or, through inaction, allow humanity to come to harm." 1 st law-updated: A robot must not harm a human being or, through inaction allow one to come to harm, unless this would violate the 0 th law." 2 nd law: A robot must always obey human beings unless that is in conflict with the 1 st law. 3 rd law: A robot must protect itself from harm unless that is in conflict with the 1 st and 2 nd law. [*] I. Asimov, Robots and Empire, Doubleday & Co., NY 1985, p.291 Moral, Ethical, Theological, Legal, Biological, Psychological Social, Economic Challenges Cyborg Society/ World {Cyborgs} Cyber/Machine Society/World {Intelligent Androis} Human Society/World {Human Beings}

72 Thank You!