communication and alignment of grounded symbolic knowledge among heterogeneous robots ...

268 Pages·2010·4.99 MB·English

COMMUNICATION AND ALIGNMENT
OF GROUNDED SYMBOLIC KNOWLEDGE
AMONG HETEROGENEOUS ROBOTS

A Dissertation
Presented to
The Academic Faculty

by

Zsolt Kira

In Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy in Computer Science in the
School of Interactive Computing
College of Computing

Georgia Institute of Technology
May 2010

COPYRIGHT 2010 BY ZSOLT KIRA
COMMUNICATION AND ALIGNMENT
OF GROUNDED SYMBOLIC KNOWLEDGE
AMONG HETEROGENEOUS ROBOTS

Approved by:

Dr. Ronald C. Arkin, Advisor Dr. Tucker Balch
School of Interactive Computing School of Interactive Computing
College of Computing College of Computing
Georgia Institute of Technology Georgia Institute of Technology

Dr. Thomas R. Collins Dr. Ashok K. Goel
Georgia Tech Research Institute School of Interactive Computing
College of Computing
Georgia Institute of Technology

Dr. Charles L. Isbell, Jr.
School of Interactive Computing
College of Computing
Georgia Institute of Technology

Date Approved: April 5, 2010
for my wife, my family, and my friends,
whose support made this possible
ACKNOWLEDGEMENTS
First, I would like to thank my advisor, Dr. Ron Arkin, whose support has made this
dissertation possible. His critical reading of this work continually challenged me to
improve upon it, and I think the result is much improved and much more accessible as a
result. His vast knowledge of the field has been extremely helpful during the exploration
process that has led to this topic.
I would also like to thank the rest of the committee: Tucker Balch, Tom Collins,
Ashok Goel, and Charles Isbell. The many helpful comments, especially during the
formative stage of this work, helped me to better shape and define a substantive topic in
robotics.
Without my family, this dissertation would have never been possible. My parents
were inspirational in their ability to start from nothing and achieve success despite the
challenges. They continually pushed me to work hard and excel at whatever I do, and
without this push I would have never reached this stage. My brother, who always
provided a positive role model that I could follow, was always supportive of my goals
and was there for me when I needed help, no matter what the situation was. Thank you,
family, for everything you have done for me.
I don’t think I can ever thank my wife enough for her eternal patience, love, and
support that she has shown throughout the process of writing this dissertation. Her
sacrifices that allowed me to chase my dreams were great, and deeply appreciated. She
never let me falter and constantly reminded me what I was working towards.
iv
Special thanks go to Dr. Stephen Murrell, an incredible teacher and influential mentor
while I was at the University of Miami. I would probably have not tread down this path
had his Artificial Intelligence class not inspired me.
During my journey at Georgia Tech, I have met many amazing, intelligent, and fun-to-
be-with friends and colleagues. They made the process much more enjoyable, and
provided endless support, encouragement, and advice. To Patrick Ulam and Alan
Wagner, thanks for the friendship from day one, filled with long discussions about
robotics and life, lots of fun, and memorable trips. Going through the process in parallel
with close friends, from start to finish, makes everything more enjoyable and helps to
keep one’s sanity. Endo Yoichiro was a great friend and constant leader in the lab, and
his help was invaluable in learning the ropes. To Raffay Hamid and Maya Cakmak,
thanks for the deep conversations, much-needed fun outside of school, and advice that
always made sense. To Ana Fiallos, a friend for almost a decade, thanks for the
invaluable friendship and for listening to all of my venting. I would also like to thank the
many friends and colleagues that have, in some form or another, shaped my life at
Georgia Tech: Ananth Rangathan, “Breakthrough” George Baah, Michael Kaess, Alex
Stoytchev, Eric Martinson, Lilia Moshkina, Sung Hyun Park, and Mattia Castelnovi, all
of whom provided great company at the Mobile Robot Lab; and Arya Irani, Soorajh Bhat,
Keith O’ Hara, and Matt Powers, for providing friendship and perspectives from outside
the lab.
Finally, I greatly enjoyed all of my summer internships at the Navy Center for Applied
Research in Artificial Intelligence at the Naval Research Laboratory. They broadened
my perspective of the field and gave me invaluable experience. I would like to thank
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Alan Schultz and Mitchell Potter for giving me the opportunity to be a part of this great
laboratory, as well as the many friends and colleagues I met there: Don Sofge, Bob
Daley, David Aha, Paul Wiegand, Ben Fransen, Brandon James, Bill Spears, Wende
Frost, Frederick Heckel, Sam Blissard, Magda Bugaska, and Jeff Bassett.
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TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS iv
LIST OF TABLES xiii
LIST OF FIGURES xvii
SUMMARY xxv
CHAPTER
1 INTRODUCTION 1
1.1 Applications 3
1.2 Defining the Problem 6
1.3 Research Questions 9
1.4 Contributions 9
1.5 Dissertation Outline 12
2 BACKGROUND AND RELATED WORK 12
2.1 Physical and Social Symbol Grounding 16
2.1.1 Physical Symbol Grounding 16
2.1.2 Social Symbol Grounding 19
2.1.3 Symbol Grounding in Robotic Systems 23
2.1.4 Shared or Joint Attention 24
2.1.5 Impact of Heterogeneity on Sharing of Multi-Level 25
2.1.6 Ontology Alignment in Multi-Agent Systems 26
2.2 Knowledge Transfer and Analogy 29
2.2.1 In Multi-Agent Systems 30
2.2.2 Knowledge Transfer In Case-Based Reasoning 31
vii
2.2.3 Knowledge Sharing In Robotic Systems 36
2.2.4 Unexplored Territory 38
2.3 Context, Common Ground, and Application to HRI 39
2.3.1 Psychological Studies of Human Dialogue and Common Ground
40
2.3.2 Applications to HRI 41
2.3.3 Differences Between Human-Robot and Robot-Robot Interaction
43
2.4 Defining or Characterizing Capabilities and Heterogeneity 45
2.5 Summary 46
3 CONCEPTUAL SPACES: A GROUNDED CONCEPT REPRESENTATION 48
3.1 The Problem of Heterogeneity: A Motivating Experiment 48
3.2 Representation Overview 58
3.3 Sensors, Features, Properties, and Concepts 61
3.4 Calculating Concept Memberships and Concept Similarity 68
3.5 Learning Properties and Concepts from Observation 71
3.6 Defining Perceptual Heterogeneity 76
3.7 Experimental Platforms 81
3.7.1 Simulated Platforms 81
3.7.2 Real-Robot Platforms (Configuration 1) 83
3.7.3 Real-Robot Platforms (Configuration 2) 83
3.7.4 Processing 84
3.8 Experimental Results: Property and Concept Learning 85
3.8.1 Hypothesis 86
3.8.2 Performance Metrics 86
3.8.3 Simulation 87
viii
3.8.3.1 Procedure 87
3.8.3.2 Results 92
3.8.4 Real-Robot (Configuration 2) 93
3.8.4.1 Procedure 93
3.8.4.2 Results 96
3.8.4.3 Discussion 96
3.9 Experimental Results: Heterogeneity and Direct Property Transfer 97
3.9.1 Hypothesis 97
3.9.2 Procedure 98
3.9.3 Results 100
3.10 The Importance of Property Abstractions for Learning 102
3.10.1 Hypothesis 103
3.10.2 Procedure 103
3.10.3Results & Discussion 106
3.11 Summary 108
4 BUILDING MODELS OF PROPERTY AND CONCEPTUAL DIFFERENCES
IN MULTIPLE ROBOTS 111
4.1 Sources of Heterogeneity between Two Robots 111
4.2 Modeling Differences in Properties 112
4.2.1 Confusion Matrices 113
4.3 Modeling Differences in Concepts 118
4.4 Locally-Shared Context 118
4.5 Experimental Evaluation: Building Property Mappings 120
4.5.1 Hypothesis 121
4.5.2 Real Robot Results (Configuration 1) 122
4.5.2.1 Procedure 122
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4.5.2.2 Results 123
4.5.3 Real Robot Results (Configuration 2) 125
4.5.4 Simulation Experiments 128
4.6 Summary 129
5 CONCEPT TRANSFER USING PROPERTY MAPPINGS 130
5.1 Perceptual Heterogeneity: The Space of Possibilities 130
5.2 Ontology Alignment with Shared Properties 133
5.3 Sources of Error in Concept Transfer 134
5.4 Transferring Concepts in Conceptual Spaces 136
5.5 Experimental Evaluation Overview 138
5.6 Experimental Results: The Importance of Property Abstractions for
Transfer 139
5.6.1 Hypothesis 139
5.6.2 Procedure 139
5.6.3 Results 140
5.6.4 Discussion 150
5.7 Experimental Results: Concept Transfer when using Conceptual Spaces
152
5.7.1 Hypothesis 153
5.7.2 Simulation Results 153
5.7.2.1 Procedure 153
5.7.2.2 Results 154
5.7.3 Real-Robot Results (Configuration 2) 156
5.7.3.1 Procedure 156
5.7.3.2 Results 157
5.8 Experimental Results: Estimating Post-Transfer Performance 161
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