Abstract
Social robotics is an exciting field with too many research threads within which interesting new developments appear every year. It is very hard to summarize what a field as varied and interdisciplinary as social robotics is targeting but we can distinguish two main research directions within the field.
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References
Bartneck C, Kanda T, Norihiro Hagita HI (2009) My robotic doppelganger: a critical look at the uncanny valley. In: RO-MAN’09: IEEE international symposium on robot and human interactive communication, IEEE, pp 269–276
Blumberg BM, Galyean TA (1995) Multi-level direction of autonomous creatures for real-time virtual environments. In: The 22nd annual conference on computer graphics and interactive techniques, ACM, pp 47–54
Breazeal C, Aryananda L (2002) Recognition of affective communicative intent in robot-directed speech. Auton Robots 12(1):83–104
Breazeal C, Berlin M, Brooks A, Gray J, Thomaz AL (2006) Using perspective taking to learn from ambiguous demonstrations. Robot Auton Syst 54(5):385–393
Breazeal C, Buchsbaum D, Gray J, Gatenby D, Blumberg B (2005a) Learning from and about others: towards using imitation to bootstrap the social understanding of others by robots. Artif Life 11(1/2):31–62
Breazeal C, Hoffman G, Lockerd A (2004) Teaching and working with robots as a collaboration. In: AAMAS’04: the 3rd international joint conference on autonomous agents and multiagent systems, vol 3. IEEE Computer Society, pp 1030–1037
Carruthers P, Smith PK (1996) Theories of Theories of mind. Cambridge University Press
Dautenhahn K, Billard A (1999) Bringing up robots or the psychology of socially intelligent robots: from theory to implementation. In: The third annual conference on autonomous agents, ACM, pp 366–367
Demiris Y, Dearden A (2005) From motor babbling to hierarchical learning by imitation: a robot developmental pathway. In: The 5th international workshop on epigenetic robotics: modeling cognitive development in robotic systems, Lund University Cognitive Studies, pp 31–37
Demiris Y, Hayes G (2002) Imitation as a dual-route process featuring predictive and learning components: a biologically plausible computational model. 13:327–361
Demiris Y, Johnson M (2003) Distributed, predictive perception of actions: a biologically inspired robotics architecture for imitation and learning. Connect Sci 15(4):231–243
Demiris Y, Khadhouri B (2006) Hierarchical attentive multiple models for execution and recognition of actions. Robot Auton Syst 54(5):361–369
Fong T, Nourbakhsh I, Dautenhahn K (2003) A survey of socially interactive robots. Robot Auton Syst 42(3):143–166
Ishiguro H, Kanda T, Kimoto K, Ishida T (1999) A robot architecture based on situated modules. In: IROS’99: IEEE/RSJ conference on intelligent robots and systems, vol 3. IEEE, pp 1617–1624
Ishiguro H, Ono T, Imai M, Maeda T, Kanda T, Nakatsu R (2001) Robovie: a robot generates episode chains in our daily life. In: ISR’01: the 32nd international symposium on robotics, vol 19, pp 1356–1361
Isla D, Burke R, Downie M, Blumberg B (2001) A layered brain architecture for synthetic creatures. In: IJCAI’01: international joint conference on artificial intelligence, vol 17. Citeseer, pp 1051–1058
Kanda T, Ishiguro H, Imai M, Ono T (2004) Development and evaluation of interactive humanoid robots. Proc IEEE 92(11):1839–1850
Kanda T, Kamasima M, Imai M, Ono T, Sakamoto D, Ishiguro H, Anzai Y (2007) A humanoid robot that pretends to listen to route guidance from a human. Auton Robots 22(1):87–100
Komatsu T, Yamada S (2007) How do robotic agents’ appearances affect people’s interpretations of the agents’ attitudes? In: CHI’07: ACM conference on human factors in computing systems, ACM, pp 1935–1940
Kozima H, Yano H (2001) A robot that learns to communicate with human caregivers. In: The first international workshop on epigenetic robotics, pp 47–52
Liu P, Glas DF, Kanda T, Norihiro Hagita HI (2014) How to train your robot: teaching service robots to reproduce human social behavior. In: RO-MAN’14: the 23rd IEEE international symposium on robot and human interactive communication, IEEE, pp 961–968
Lockerd A, Breazeal C (2004) Tutelage and socially guided robot learning. In: IROS’04: IEEE/RSJ international conference on intelligent robots and systems, vol 4. IEEE, pp 3475–3480
MacDorman KF, Ishiguro H (2006) The uncanny advantage of using androids in cognitive and social science research. Interac Stud 7(3):297–337
Meltzoff AN, Moore MK (1997) Explaining facial imitation: a theoretical model. Early Dev Parent 6(34):179–192
Mori M, MacDorman KF, Kageki N (2012) The uncanny valley [from the field]. IEEE Robot Autom Mag 19(2):98–100
Ramey CH (2005) The uncanny valley of similarities concerning abortion, baldness, heaps of sand, and humanlike robots. In: Views of the uncanny valley workshop: IEEE/RAS international conference on humanoid robots, pp 8–13
Saygin AP, Chaminade T, Ishiguro H (2010) The perception of humans and robots: uncanny hills in parietal cortex. In: CogSci’10: the 32nd annual conference of the cognitive science society, pp 2716–2720
Sutton RS, Barto AG (1998) Reinforcement learning: an introduction, vol 1. MIT Press
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Mohammad, Y., Nishida, T. (2015). Introduction to Social Robotics. In: Data Mining for Social Robotics. Advanced Information and Knowledge Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-25232-2_6
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DOI: https://doi.org/10.1007/978-3-319-25232-2_6
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