Behavior, Dialog and Learning

1. Behavior, Dialog and Learning • The dialog/behavior has the following components: • (1) Eliza-like natural language dialogs based on pattern matching and limited parsing. • Commercial products like Memoni, Dog.Com, Heart, Alice, and Doctor all use this technology, very successfully – for instance Alice program won the 2001 Turing competition. • This is a “conversational” part of the robot brain, based on pattern-matching, parsing and black-board principles. • It is also a kind of “operating system” of the robot, which supervises other subroutines.

2. Behavior, Dialog and Learning • (2) Subroutines with logical data base and natural language parsing (CHAT). • This is the logical part of the brain used to find connections between places, timings and all kind of logical and relational reasonings, such as answering questions about Japanese geography. • (3) Use of generalization and analogy in dialog on many levels. • Random and intentional linking of spoken language, sound effects and facial gestures. • Use of Constructive Induction approach to help generalization, analogy reasoning and probabilistic generations in verbal and non-verbal dialog, like learning when to smile or turn the head off the partner.

3. Behavior, Dialog and Learning • (4) Model of the robot, model of the user, scenario of the situation, history of the dialog, all used in the conversation. • (5) Use of word spotting in speech recognition rather than single word or continuous speech recognition. • (6) Continuous speech recognition (Microsoft) • (7) Avoidance of “I do not know”, “I do not understand” answers from the robot. • Our robot will have always something to say, in the worst case, over-generalized, with not valid analogies or even nonsensical and random.

4. Constructive Induction

6. Example “Age Recognition” Examples of data for learning, four people, given to the system

7. Example “Age Recognition” Encoding of features, values of multiple-valued variables

8. Groups show a simple induction from the Data Multi-valued Map for Data d = F( a, b, c )

9. Groups show a simple induction from the Data Old people smile rarely blonde hair Grey hair Middle-age people smile moderately Teenagers smile often Children smile very often

10. Another example: teaching movements Input variables Output variables

11. Generalization of the Ashenhurst-Curtis decomposition model

12. This kind of tables known from Rough Sets, Decision Trees, etc Data Mining

13. Original table Second variant First variant of decomposition At every step many decompositions exist Decomposition is hierarchical Which decomposition is better?

14. Constructive Induction: Technical Details • U. Wong and M. Perkowski, A New Approach to Robot’s Imitation of Behaviors by Decomposition of Multiple-Valued Relations, Proc. 5th Intern. Workshop on Boolean Problems, Freiberg, Germany, Sept. 19-20, 2002, pp. 265-270. • A. Mishchenko, B. Steinbach and M. Perkowski, An Algorithm for Bi-Decomposition of Logic Functions, Proc. DAC 2001, June 18-22, Las Vegas, pp. 103-108. • A. Mishchenko, B. Steinbach and M. Perkowski, Bi-Decomposition of Multi-Valued Relations, Proc. 10th IWLS, pp. 35-40, Granlibakken, CA, June 12-15, 2001. IEEE Computer Society and ACM SIGDA.

15. Constructive Induction • Decision Trees, Ashenhurst/Curtis hierarchical decomposition and Bi-Decomposition algorithms are used in our software • These methods create our subset of MVSIS system developed under Prof. Robert Brayton at University of California at Berkeley [2]. • The entire MVSIS system can be also used. • The system generates robot’s behaviors (C program codes) from examples given by the users. • This method is used for embedded system design, but we use it specifically for robot interaction.

16. Ashenhurst Functional Decomposition A - free set Evaluates the data function and attempts to decompose into simpler functions. F(X) = H( G(B), A ), X = A  B X B - bound set if A  B = , it is disjoint decomposition if A  B  , it is non-disjoint decomposition

17. A Standard Map of function ‘z’ Explain the concept of generalized don’t cares Bound Set a b \ c Columns 0 and 1 and columns 0 and 2 are compatible column compatibility = 2 Free Set z

18. Relation NEW Decomposition of Multi-Valued Relations F(X) = H( G(B), A ), X = A  B A X Relation Relation B if A  B = , it is disjoint decomposition if A  B  , it is non-disjoint decomposition

19. Bound Set a b \ c C0 C1 Free Set C2 Forming a CCG from a K-Map Columns 0 and 1 and columns 0 and 2 are compatible column compatibility index = 2 Column Compatibility Graph z

20. Forming a CIG from a K-Map a b \ c C0 C1 C2 z Columns 1 and 2 are incompatible chromatic number = 2 Column Incompatibility Graph

21. Constructive Induction • A unified internal language is used to describe behaviors in which text generation and facial gestures are unified. • This language is for learned behaviors. • Expressions (programs) in this language are either created by humans or induced automatically from examples given by trainers.

22. Braitenberg Vehicles and Quantum Automata Robots

23. Another Example: Braitenberg Vehicles and Quantum BV

24. Braitenberg Vehicles

25. |0 |1 |x |0 |1 |x = V V† V U Quantum Circuits Toffoli gate: Universal, uses controlled square root of NOT Example 1: Simulation |0 |1 |x |0 |1 V|x |0 |1 |0 |1 |x |0 |1 |0 |1 |x ?

26. Quantum Portland Faces

27. Conclusion. What did we learn • (1) the more degrees of freedom the better the animation realism. Art and interesting behavior above certain threshold of complexity. • (2) synchronization of spoken text and head (especially jaw) movements are important but difficult. Each robot is very different. • (3) gestures and speech intonation of the head should be slightly exaggerated – superrealism, not realism.

28. Conclusion. What did we learn(cont) • (4) Noise of servos: • the sound should be laud to cover noises coming from motors and gears and for a better theatrical effect. • noise of servos can be also reduced by appropriate animation and synchronization. • (5) TTS should be enhanced with some new sound-generating system. What? • (6) best available ATR and TTS packages should be applied. • (7) OpenCV from Intel is excellent. • (8) use puppet theatre experiences. We need artists. The weakness of technology can become the strength of the art in hands of an artist.

29. Conclusion. What did we learn(cont) • (9) because of a too slow learning, improved parameterized learning methods should be developed, but also based on constructive induction. • (10) open question: funny versus beautiful. • (11) either high quality voice recognition from headset or low quality in noisy room. YOU CANNOT HAVE BOTH WITH CURRENT ATR TOOLS. • (12) low reliability of the latex skins and this entire technology is an issue.

30. Robot shows are exciting We won an award in PDXBOT 2004. We showed our robots to several audiences Our Goal is to build toys for 21-st Century and in this process, change the way how engineers are educated. International Intel Science Talent Competition and PDXBOT 2004, 2005

31. Commercial Value of Robot Toys and Theatres

32. Robot Toy Market - Robosapiens toy, poses in front of toy, poses in front of toy, poses in front of

33. Globalization • Globalization implies that images, technologies and messages are everywhere, but at the same time disconnected from a particular social structure or context. (Alain Touraine) • The need of a constantly expanding market for its products chases the bourgoise over the whole surface of the globe. It must nestle everywhere, settle everywhere, establish connections everywhere. (Marx & Engels, 1848)

34. India and China - what’s different? • They started at the same level of wealth and exports in 1980 • China today exports $ 184 Bn vs $ 34 Bn for India • China’s export industry employs today over 50 million people (vs 2 m s/w in 2008, and 20 m in the entire organized sector in India today!) • China’s export industry consists of toys (> 60% of the world market), bicycles (10 m to the US alone last year), and textiles (a vision of having a share of > 50% of the world market by 2008)

35. Learning from Korea and Singapore • The importance of Learning • To manufacture efficiently • To open the door to foreign technology and investment • To have sufficient pride in ones own ability to open the door and go out and build ones own proprietary identity • To invest in fundamentals like Education • to have the right cultural prerequisites for catching up • To have pragmatism rule, not ideology

36. Samsung 1979 Started making microwaves 1980 First export order (foreign brand) 1983 OEM contracts with General Electric 1985 All GE microwaves made by Samsung 1987 All GE microwaves designed by Samsung 1990 The world’s largest microwave manufacturer - without its own brand 1990 Launch own brand outside Korea • Samsung microwaves # 1 worldwide, twelve factories in twelve countries (including India, China and the US) 2003 – the largest electronics company in the world

37. How did Samsung do it? • By learning from GE and other buyers • By working very hard - 70 hour weeks, 10 days holiday • By being very productive - 9 microwaves per person per day vs 4 at GE • By meeting every delivery on time, even if it meant working 7-day weeks for six months • By developing new models so well that it got GE to stop developing their own

38. Fundamental question for humanoid robot builders

39. Should we build humanoid robots? • Man’s design versus robot’s design • The humanoid robot is versatile and adaptive, it takes its form from a human, a design well-verified by Nature. • Complete isomorphism of a humanoid robot with a human is very difficult to achieve (walking) and not even not entirely desired. • All what we need is to adapt the robot maximally to the needs of humans – elderly, disabled, children, entertainment. • Replicating human motor or sensor functionality are based on mechanistic methodologies, • but adaptations and upgrades are possible – for instance brain wave control or wheels • Is it immoral?

40. Is it worthy to build humanoid robots? • Can building a mechanistic digital synthetic version of man be anything less than a cheat when man is not mechanistic, digital nor synthetic?  • If reference for the “ultimate” robot is man, then there is little confusion about one’s aim to replace man with a machine.

41. Man & Machine • Main reason to build machines in our likeness is to facilitate their integration in our social space: • SOCIAL ROBOTICS • Robot should do many things that we do, like climbing stairs, but not necessarily in the way we do it – airplane and bird analogy. • Humanoid robots/social robots should make our life easier.

42. The Social Robot • “developing a brain”: • Cognitive abilities as developed from classical AI to modern cognitive ideas (neural networks, multi-agent systems, genetic algorithms…) • “giving the brain a body”: • Physical embodiment, as indicated by Brooks [Bro86], Steels [Ste94], etc. • “a world of bodies”: • Social embodiment • A Social Robot is: • A physical entity embodied in a complex, dynamic, and social environment sufficiently empowered to behave in a manner conducive to its own goals and those of its community.

43. Anthropomorphism • Social interaction involves an adaptation on both sides to rationalise each others actions, and the interpretation of the others actions based on one’s references • Projective Intelligence:the observer ascribes a degree of “intelligence” to the system through their rationalisation of its actions

44. Anthropomorphism & The Social Robot • Objectives • Augment human-robot sociality • Understand and rationalize robot behavior • Embrace anthropomorphism • BUT - How does the robot not become trapped by behavioral expectations? • REQUIRED: A balance between anthropomorphic features and behaviors leading to the robot’s ownidentity

45. Finding the Balance • Movement • Behavior (afraid of the light) • Facial Action Coding System • Form • Physical construction • Degrees of freedom • Interaction • Communication (robot-like vs. human voice) • Social cues/timing • Autonomy • Function & role • machine vs. human capabilities

46. Humanoid Robots Experiments and Research Tasks • Autonomous mobile robots • Emotion through motion • “Projective emotion” • Anthropomorphism • Social behaviors • Qualitative and quantitative analysis to a wide audience through online web-based experiments

47. The perception learning tasks • Robot Vision: • Where is a face? (Face detection) • Who is this person (Face recognition, learning with supervisor, person’s name is given in the process. • Age and gender of the person. • Hand gestures. • Emotions expressed as facial gestures (smile, eye movements, etc) • Objects hold by the person • Lips reading for speech recognition. • Body language.

48. The perception learning tasks • Speech recognition: • Who is this person (voice based speaker recognition, learning with supervisor, person’s name is given in the process.) • Isolated words recognition for word spotting. • Sentence recognition. • Sensors. • Temperature • Touch • movement

49. The behavior learning tasks • Facial and upper body gestures: • Face/neck gesticulation for interactive dialog. • Face/neck gesticulation for theatre plays. • Face/neck gesticulation for singing/dancing. • Hand gestures and manipulation. • Hand gesticulation for interactive dialog. • Hand gesticulation for theatre plays. • Hand gesticulation for singing/dancing.

50. Learning the perception/behavior mappings • Tracking the human. • Full gesticulation as a response to human behavior in dialogs and dancing/singing. • Modification of semi-autonomous behaviors such as breathing, eye blinking, mechanical hand withdrawals, speech acts as response to person’s behaviors. • Playing games with humans. • Body contact with human such as safe gesticulation close to human and hand shaking.