Robotic Cars

Report
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 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. (Email 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).
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Topics we covered
• Turing Test
• Uninformed search
– Methods
– Completeness, optimality
– Time complexity
• Informed search
– Heuristics
– Admissibility of heuristics
– A* search
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• 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
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• 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
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• 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)
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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
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Robotic Cars
• 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
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From S. Thrun,
Towards Robotic Cars
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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.
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Examples of Components of Stanley / Junior
• Obstacles: Where are they?
– Static obstacles: Build “occupancy grid maps”
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– Moving obstacles:
Identify with “temporal
differencing” with
sequential laser scans,
and then use “particle
filtering” to track
– “Particle filter” –
related to Hidden
Markov Model
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Particle Filters for Tracking Moving Objects
From http://cvlab.epfl.ch/teaching/topics/
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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.”
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From M. Montemerlo et al., Junior: The Stanford Entry in the Urban Challenge
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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.
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From M. Montemerlo et al., Junior: The Stanford Entry in the Urban Challenge
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Examples of Components of Stanley / Junior
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New York Times:
“Google lobbies Nevada to allow self-driving cars”
http://www.nytimes.com/2011/05/11/science/11drive.html
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Sociable Robotics
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Kismet
Kismet and Rich
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What can Kismet do?
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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”
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Overview and Hardware
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Expressions examples
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From Recognition of Affective Communicative Intent in Robot-Directed Speech
C. BREAZEAL AND L. ARYANANDA
Perceiving “affective intent”
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From Recognition of Affective Communicative Intent in Robot-Directed Speech
C. BREAZEAL AND L. ARYANANDA
Perceiving “affective intent”
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Perceiving affective intent
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From A context-dependent attention system for a social robot
C. Breazeal and B. Scassellati
Vision system
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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
Saliency map
Pre-attentive filters
• 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, …
Vision System: Attention
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From people.csail.mit.edu/paulfitz/present/social-constraints.ppt
Internal influences on attention
“Seek toy” –
“Seek face” –
low skin gain, high saturated-color gain
Looking time 28% face, 72% block
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
Attention: Gaze direction
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Attention System
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From people.csail.mit.edu/paulfitz/present/social-constraints.ppt
Negotiating interpersonal distance
Person
backs off
Person draws
closer
Too close –
Comfortable
withdrawal
response interaction distance
Too far –
calling
behavior
Beyond
sensor
range
• Robot establishes a “personal space” through
expressive cues
• Tunes interaction to suit its vision capabilities
Negotiating personal space
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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
Negotiating object showing
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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
Conversational turn-taking
Web page for all these videos:
http://www.ai.mit.edu/projects/sociable/videos.html
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How to evaluate Kismet?
What are some applications for Kismet and its
descendants?
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Leonardo
http://www.youtube.com/watch?v=ilmDN2e_Flc
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