1. Announcements HW 6: Written (not programming) assignment. –Assigned today; Due Friday, Dec. 9. E-mail to me. Quiz 4 : OPTIONAL: Take home quiz, open book. –If youre happy with your quiz grades so far, you dont have to take it. (Grades from the four quizzes will be averaged.) –Assigned Wednesday, Nov. 30; due Friday, Dec. 2 by 5pm. (E- mail or hand in to me.) –Quiz could cover any material from previous quizzes. –Quiz is designed to take you one hour maximum (but you have can work on it for as much time as you want, till Friday, 5pm). 1

2. Topics we covered Turing Test Uninformed search –Methods –Completeness, optimality –Time complexity Informed search –Heuristics –Admissibility of heuristics –A* search 2

3. Game-playing –Notion of a game tree, ply –Evaluation function –Minimax –Alpha-Beta pruning Natural-Language Processing –N-grams –Naïve Bayes for text classification –Support Vector Machines for text classification –Latent semantic analysis –Watson question-answering system –Machine translation 3

4. Speech Recognition –Basic components of speech-recognition system Perceptrons and Neural Networks –Perceptron learning and classification –Multilayer perceptron learning and classification Genetic Algorithms –Basic components of a GA –Effects of parameter settings Vision –Content-Based Image Retrieval –Object Recogition 4

5. Analogy-Making –Basic components of Copycat, as described in the slides and reading Robotics –Robotic Cars (as described in the reading) –Social Robotics (as described in the reading) 5

6. Reading for this week (links on the class website) S. Thrun, Toward Robotic Cars C. Breazeal, Toward Sociable Robotics R. Kurzweil, The Singularity is Near: Book Precis D. McDermott, Kurzweil's argument for the success of AI 6

7. Robotic Cars 7 http://www.ted.com/talks/sebastian_thrun_google_s_driverless_car.html http://www.youtube.com/watch?v=lULl63ERek0 http://www.youtube.com/watch?v=FLi_IQgCxbo

8. 8 From S. Thrun, Towards Robotic Cars

9. Examples of Components of Stanley / Junior Localization: Where am I? –Establish correspondence between cars present location and a map. –GPS does part of this but can have estimation error of > 1 m. –To get better localization, relate features visible in laser scans to map features. 9

10. Obstacles: Where are they? –Static obstacles: Build occupancy grid maps 10 Examples of Components of Stanley / Junior

11. –Moving obstacles: Identify with temporal differencing with sequential laser scans, and then use particle filtering to track –Particle filter – related to Hidden Markov Model 11

12. 12 Particle Filters for Tracking Moving Objects From http://cvlab.epfl.ch/teaching/topics/

13. Path planning: –Structured navigation (on road with lanes): Junior used a dynamic-programming-based global shortest path planner, which calculates the expected drive time to a goal location from any point in the environment. Hill climbing in this dynamic-programming function yields paths with the shortest expected travel time. 13 Examples of Components of Stanley / Junior

14. 14 From M. Montemerlo et al., Junior: The Stanford Entry in the Urban Challenge

15. –Unstructured navigation (e.g., parking lots, u-turns) Junior used a fast, modified version of the A* algorithm. This algorithm searches shortest paths relative to the vehicles map, using search trees. 15 Examples of Components of Stanley / Junior

16. 16 From M. Montemerlo et al., Junior: The Stanford Entry in the Urban Challenge

17. 17 Examples of Components of Stanley / Junior

18. 18

19. New York Times: Google lobbies Nevada to allow self-driving cars http://www.nytimes.com/2011/05/11/science/11drive.html 19

20. 20 Sociable Robotics

21. 21 Kismet Kismet and Rich

22. What can Kismet do? 22

23. What can Kismet do? Vision Visual attention Speech recognition (emotional tone) Speech production (prosody) Speech turn-taking Head and face movements Facial expression Keeping appropriate personal space 23

24. 24 Overview and Hardware

25. 25 Expressions examples

26. 26 From Recognition of Affective Communicative Intent in Robot-Directed Speech C. BREAZEAL AND L. ARYANANDA Perceiving affective intent

27. 27 From Recognition of Affective Communicative Intent in Robot-Directed Speech C. BREAZEAL AND L. ARYANANDA Perceiving affective intent

28. 28 Perceiving affective intent

29. 29 From A context-dependent attention system for a social robot C. Breazeal and B. Scassellati Vision system

30. Skin tone ColorMotionHabituation Weighted by behavioral relevance Pre-attentive filters External influences on attention Attention is allocated according to salience Salience can be manipulated by shaking an object, bringing it closer, moving it in front of the robots current locus of attention, object choice, hiding distractors, … Current input Saliency map From people.csail.mit.edu/paulfitz/present/social-constraints.ppt

31. 31 Vision System: Attention

32. Seek face – high skin gain, low color saliency gain Looking time 28% face, 72% block Seek toy – low skin gain, high saturated-color gain Looking time 28% face, 72% block Internal influences on attention Internal influences bias how salience is measured The robot is not a slave to its environment From people.csail.mit.edu/paulfitz/present/social-constraints.ppt

33. 33 Attention: Gaze direction

34. 34 Attention System

35. Comfortable interaction distance Too close – withdrawal response Too far – calling behavior Person draws closer Person backs off Beyond sensor range Negotiating interpersonal distance Robot establishes a personal space through expressive cues Tunes interaction to suit its vision capabilities From people.csail.mit.edu/paulfitz/present/social-constraints.ppt

36. 36 Negotiating personal space

37. Negotiating object showing Robot conveys preferences about how objects are presented to it through irritation, threat responses Again, tunes interaction to suit its limited vision Also serves protective role Comfortable interaction speed Too fast – irritation response Too fast, Too close – threat response From people.csail.mit.edu/paulfitz/present/social-constraints.ppt

38. 38 Negotiating object showing

39. Turn-Taking Cornerstone of human-style communication, learning, and instruction Phases of turn cycle –Listen to speaker: hold eye contact –Reacquire floor: break eye contact and/or lean back a bit –Speak: vocalize –Hold the floor: look to the side –Stop ones speaking turn: stop vocalizing and re-establish eye contact –Relinquish floor: raise brows and lean forward a bit Adapted from people.csail.mit.edu/paulfitz/present/social-constraints.ppt

40. Conversational turn-taking

41. Web page for all these videos: http://www.ai.mit.edu/projects/sociable/videos.html 41

42. How to evaluate Kismet? What are some applications for Kismet and its descendants? 42

43. Leonardo http://www.youtube.com/watch?v=ilmDN2e_Flc http://www.youtube.com/watch?v=ilmDN2e_Flc 43