PhD Document - Universidad de Las Palmas de Gran Canaria

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CHAPTER 1. INTRODUCTION and surprise. Feelix implements two models of emotional interaction and expression inspired by psychological theories about emotions in humans. Tactile sensations translate directly into emotions. This low perceptual bandwidth does not require attentional mechanisms, which would be necessary with more complex sensors such as vision. Advanced perception techniques are obviously necessary for successful interaction. SIG [Okuno et al., 2002] is a humanoid robot designed as a test bed of integration of perceptual information to control a large number of degrees of freedom. The visual perceptual subsystem is in charge of a stereo vision system, each camera having 3 DOF. The most re- markable aspect of SIG is its sound localization ability. It uses a total of four microphones. The body is covered with an isolating material. Two of the microphones are placed inside and the other two outside. This allows the robot to cancel the noise of its own motors (see Section 5.2). The ability to talk to multiple users in the same session is addressed in ROBITA [Matsusaka et al., 1999]. This poses many problems, such as recognition of who is speaking and to whom he is speaking. To solve them, the robot implements face recognition, face direction recognition, sound localization, speech recognition and gestural output. It has 2 CCD cameras on his head, 2 DOF for each of them and 2 DOF in the neck. The robot uses a total of 9 computers. The availability of hardware elements has led to a profusion of social robots. A big laboratory and group of researchers is no longer necessary to complete a complex robot. There is a clear -and positive- tendency to fall into the I-want-to-build-one-too, do-it-yourself fever. Interestingly, I-want-to-build-one-too systems, despite being simple in hardware and techniques, are comparable to more complex systems. That is, excessive or advanced hardware does not seem to have a crucial effect on the overall "quality" of the robot. Two out- standing robots built with low-cost components are ARYAN and SEGURITRON, see Chap- ter 4. 1.2 Analysis The study of the available literature about social robots allows to extract a number fundamen- tal ideas. First, there seems to be a clear tendency to use psychology, ethology and infant social development studies as the main inspiration source. In fact, the development of al- most all of the robots built for social interaction revolve around one or more human models. This is the case of Kismet, for example, the design of which is inspired by infant-caregiver relationships. Other robots have used concepts like scaffolding, autism, imitation, shared 8
CHAPTER 1. INTRODUCTION attention, mindreading, empathy or emotions. In some cases, only human models have been implemented, to the detriment of other aspects like the engineering point of view (called "functional design" in [Fong et al., 2003]) or the question of the validity of known human models. Not only high-level psychological and cognitive models are being used. Neurophys- iological knowledge is also being frequently used for building social robots or modules for them. The work of Arsenio, mentioned above, is particularly significant in this respect. His PhD dissertation is in fact organized according to functional structures of the human brain, the so-called brainmap. Something similar appears in the recent work of Beltrán-González [Beltrán-González, 2005], whose work on prediction in perceptual processes for artificial systems revolves around the function of the human cerebellum. To what extent are these models, concepts and theories valid for building social robots? Are they just useful metaphors? or will, in effect, the reproduction of the models achieve similar performances as in humans? These questions are now important, as the interdisciplinary character of the problem is becoming evident. In particular, two aspects seem specially relevant: 1) the validity of human models, concepts and theories for a robot and 2) the level of detail at which those models should be reproduced. Another common feature of this type of robots is the extensive integration of different techniques and hardware. The robot designers normally choose state-of-the-art techniques for solving certain tasks and use them in the robot. Is that always the appropriate approach? Can we expect those advanced techniques, tested in other scenarios, to work well in our particular robot? On the other hand, a careful analysis of the related work may lead to the question of whether these and other robots that try to accomplish social tasks may have a robust behaviour. For industrial robots, robustness can be easily measured. That is, we can be as sure as we want that the robot will keep on working as in the test conditions. This warranty stems from the fact that the processes and algorithms that the robots implement for solving the tasks are known to be valid. As an example, consider a robot manipulator, for which precise kinematic equations have been previously obtained. Once these equations are obtained and stored in the control computer only a few tests will be necessary for us to have the guarantee that the manipulator will move to indicated points. The case seems to be different for social robots. In face recognition for example (the social ability par excellence) the number of papers describing working implementations is significantly low compared with the total. Face recognition techniques are extremely sen- sitive to illumination [Adini et al., 1997, Georghiades et al., 1998, Gross et al., 2002], hair 9
Page 1: Departamento de Informática y Sist
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Page 15 and 16: List of Figures 1.1 Abilities vs. l
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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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