Announcements. HW 6: Written (not programming) assignment. Assigned today; Due Friday, Dec. 9. to me.

Transcription

1 Announcements HW 6: Written (not programming) assignment. Assigned today; Due Friday, Dec. 9. to me. Quiz 4 : OPTIONAL: Take home quiz, open book. If you re happy with your quiz grades so far, you don t have to take it. (Grades from the four quizzes will be averaged.) Assigned Wednesday, Nov. 30; due Friday, Dec. 2 by 5pm. ( 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

8 From S. Thrun, Towards Robotic Cars 8

9 Examples of Components of Stanley / Junior Localization: Where am I? Establish correspondence between car s 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 Examples of Components of Stanley / Junior Obstacles: Where are they? Static obstacles: Build occupancy grid maps 10

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 Particle Filters for Tracking Moving Objects From 12

13 Examples of Components of Stanley / Junior 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

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

15 Examples of Components of Stanley / Junior 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 vehicle s map, using search trees. 15

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

17 Examples of Components of Stanley / Junior 17

18 18

19 New York Times: Google lobbies Nevada to allow self-driving cars 19

20 Sociable Robotics 20

21 Kismet Kismet and Rich 21

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 Overview and Hardware 24

25 Expressions examples 25

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

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

28 Perceiving affective intent 28

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

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

31 Vision System: Attention 31

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

33 Attention: Gaze direction 33

34 Attention System 34

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

36 Negotiating personal space 36

37 From people.csail.mit.edu/paulfitz/present/social-constraints.ppt Negotiating object showing Comfortable interaction speed Too fast, Too close threat response Too fast irritation response 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

38 Negotiating object showing 38

39 Adapted from people.csail.mit.edu/paulfitz/present/social-constraints.ppt 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 one s speaking turn: stop vocalizing and re-establish eye contact Relinquish floor: raise brows and lean forward a bit

40 Conversational turn-taking

41 Web page for all these videos: 41

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

43 Leonardo 43