PhD Document - Universidad de Las Palmas de Gran Canaria

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7.5 Priorities assigned to actions. Actions that do not appear in the table have all an equal priority value of 0. . . . . . . . . . . . . . . . . . . . . . . . . . . 130 7.6 Behaviours implemented in ZagaZ. . . . . . . . . . . . . . . . . . . . . . . 131 8.1 Example interaction: one person interacting with the robot. Behaviours in bold were the ones executed by the robot. . . . . . . . . . . . . . . . . . . 141 8.2 Example interaction: one person interacting with the robot. Behaviours in bold were the ones executed by the robot. . . . . . . . . . . . . . . . . . . 143 8.3 Example interaction: one person interacting with the robot plus the owner. . 144 8.4 Sentences spoken by the robot in the session of Figure 8.3, in chronological order. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 8.5 Translation of the questionnaire used to evaluate the robot. The interviewees had to give between 1 and 5 points for each question (1 means no or totally disagree, 5 means yes or totally agree. The last question allowed a free answer). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
Abstract A relatively new area for robotics research is the design of robots that can engage with humans in socially interactive situations. These robots have expressive power (i.e. they all have an expressive face, voice, etc.) as well as abilities to locate, pay attention to, and address people. In humans, these abilities fall within the ambit of what has been called "social intelligence". For this class of robots, the dominant design approach has been that of following models taken from human sciences like developmental psychology, ethology and even neurophysiology. We show that the reproduction of social intelligence, as opposed to other types of human abilities, may lead to fragile performance, in the sense of having very different per- formances between training/testing and future (unseen) conditions. This limitation stems from the fact that the abilities of the social spectrum, which appear earlier in life, are mainly unconscious to us. This is in contrast with other human tasks that we carry out using con- scious effort, and for which we can easily conceive algorithms. Thus, a coherent explanation is also given for the truism that says that anything that is easy for us is hard for robots and vice versa. For some types of robots like manipulators one can extract a set of equations (or algorithms, representations,...) that are known to be valid for solving the task. Once that these equations are stored in the control computer the manipulator will always move to desired points. Sociable robots, however, will require a much more inductive development effort. That is, the designer tests implementations in a set of cases and hopes that the performance will be equally good for unseen (future) cases. Inductive processes crucially depend on a priori knowledge: if there is little available one can have good performance in test cases but poor performance in unseen cases (overfitting). In Machine Learning, complexity penalization is often used as a principled means to avoid overfitting. Thus, we propose to develop sociable robots starting from simple algorithms and representations. Implementations should evolve mainly through extensive testing in the robot niche (the particular environment and restrictions imposed on the robot tasks, xi
Page 1: Departamento de Informática y Sist
Page 7: To my family and Gloria
Page 10 and 11: Gobierno de Canarias, Ministerio de
Page 12 and 13: 5.2.3 Implementation . . . . . . .
Page 15 and 16: List of Figures 1.1 Abilities vs. l
Page 17 and 18: 5.13 T means tracking and CS Candid
Page 19: List of Tables 1.1 The eight human
Page 23 and 24: Publications This thesis is based o
Page 25 and 26: Chapter 1 Introduction "Man is by n
Page 27 and 28: CHAPTER 1. INTRODUCTION characteris
Page 29 and 30: CHAPTER 1. INTRODUCTION robots: ➔
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Page 35 and 36: Chapter 2 The Case of Social Roboti
Page 37 and 38: CHAPTER 2. THE CASE OF SOCIAL ROBOT
Page 49 and 50: Chapter 3 Approach and Architecture
Page 51 and 52: CHAPTER 3. APPROACH AND ARCHITECTUR
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CHAPTER 4. HARDWARE OVERVIEW 4.1 Ha
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CHAPTER 4. HARDWARE OVERVIEW Figure
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CHAPTER 4. HARDWARE OVERVIEW Figure
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CHAPTER 4. HARDWARE OVERVIEW Each m
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Chapter 5 Perception "I’ve seen t
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CHAPTER 5. PERCEPTION Figure 5.1: T
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CHAPTER 5. PERCEPTION Figure 5.3: E
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CHAPTER 5. PERCEPTION we could dist
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CHAPTER 5. PERCEPTION themaximum is
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CHAPTER 5. PERCEPTION Figure 5.7: E
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CHAPTER 5. PERCEPTION Classify cues
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CHAPTER 5. PERCEPTION Figure 5.10:
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CHAPTER 5. PERCEPTION 5.3 Audio-Vis
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CHAPTER 5. PERCEPTION modules, inst
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CHAPTER 5. PERCEPTION each angle we
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CHAPTER 5. PERCEPTION hypothesis is
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CHAPTER 5. PERCEPTION M4.- Pattern
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CHAPTER 5. PERCEPTION nods and shak
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CHAPTER 5. PERCEPTION effect of thi
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CHAPTER 5. PERCEPTION effects of am
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CHAPTER 5. PERCEPTION image decreas
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CHAPTER 5. PERCEPTION with an incre
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CHAPTER 5. PERCEPTION However, we a
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CHAPTER 5. PERCEPTION where y0 is t
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CHAPTER 5. PERCEPTION be distinguis
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CHAPTER 5. PERCEPTION in the sense
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CHAPTER 5. PERCEPTION a) b) c) d) F
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CHAPTER 5. PERCEPTION the habituati
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Chapter 6 Action "CHRISTOF: We’ve
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CHAPTER 6. ACTION Expression: "Surp
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CHAPTER 6. ACTION 6.1.3 Implementat
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CHAPTER 6. ACTION Figure 6.3: Facia
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CHAPTER 6. ACTION c α error 0 o 0.
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CHAPTER 6. ACTION fear anger sorrow
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CHAPTER 6. ACTION However, we belie
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Chapter 7 Behaviour "A good name, l
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CHAPTER 7. BEHAVIOUR used defines t
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CHAPTER 7. BEHAVIOUR step environme
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CHAPTER 7. BEHAVIOUR Predicate Desc
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CHAPTER 7. BEHAVIOUR Action Descrip
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CHAPTER 7. BEHAVIOUR Name Action to
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CHAPTER 7. BEHAVIOUR ➔ Basic emot
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CHAPTER 7. BEHAVIOUR [Reilly, 1996]
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Chapter 8 Evaluation and Discussion
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CHAPTER 8. EVALUATION AND DISCUSSIO
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Chapter 9 Conclusions and Future Wo
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CHAPTER 9. CONCLUSIONS AND FUTURE W
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Appendix A CASIMIRO’s Phrase File
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APPENDIX A. CASIMIRO’S PHRASE FIL
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Bibliography [Adams et al., 2000] B
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BIBLIOGRAPHY [Bruce et al., 2001] A
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BIBLIOGRAPHY [Driscoll et al., 1998
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BIBLIOGRAPHY [Hernández et al., 20
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BIBLIOGRAPHY [Kjeldsen, 2001] R. Kj
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BIBLIOGRAPHY [Martin et al., 2000]
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BIBLIOGRAPHY [Oviatt, 2000] S. Ovia
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BIBLIOGRAPHY [Schulte et al., 1999]
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BIBLIOGRAPHY [Tyrrell, 1993] T. Tyr
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Index absolute pitch, 16 action sel
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INDEX proprioception, 39 prosopagno
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PhD Document - Universidad de Las Palmas de Gran Canaria

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