Walking partner robot chatting about scenery

References

• Burgard W, Cremers AB, Fox D, et al. The interactive museum tour-guide robot. National Conf. on Artificial Intelligence (AAAI1998), Madison, WI, USA; 1998. p. 11–18.
   Google Scholar
• Siegwart R, Arras KO, Bouabdallah S, et al. Robox at Expo.02: a large scale installation of personal robots. Rob Auton Syst. 2003;42:203–222. doi: 10.1016/S0921-8890(02)00376-7
   Web of Science ®Google Scholar
• Shiomi M, Kanda T, Ishiguro H, et al. Interactive humanoid robots for a science museum. IEEE Intell Syst. 2007;22:25–32. doi: 10.1109/MIS.2007.37
   Web of Science ®Google Scholar
• Gross H-M, Boehme H, Schroeter C, et al. TOOMAS: interactive shopping guide robots in everyday use – final implementation and experiences from long-term field trials. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS2009), St. Louis, MO, USA; 2009. p. 2005–2012.
   Google Scholar
• Karreman D, Ludden G, Evers V. Visiting cultural heritage with a tour guide robot: a user evaluation study in-the-wild. International Conference on Social Robotics, Paris, France; 2015. p. 317–326.
   Google Scholar
• Triebel R, Arras K, Alami R, et al. Spencer: a socially aware service robot for passenger guidance and help in busy airports. Field Serv Rob. 2016;113:607–622. doi: 10.1007/978-3-319-27702-8_40
   Google Scholar
• Pineau J, Montemerlo M, Pollack M, et al. Towards robotic assistants in nursing homes: challenges and results. Rob Auton Syst. 2003;42:271–281. doi: 10.1016/S0921-8890(02)00381-0
   Web of Science ®Google Scholar
• Gross H-M, Scheidig A, Debes K, et al. Roreas: robot coach for walking and orientation training in clinical post-stroke rehabilitation-rototype implementation and evaluation in field trials. Auton Robots. 2017;41:679–698. doi: 10.1007/s10514-016-9552-6
   Web of Science ®Google Scholar
• Hebesberger D, Dondrup C, Koertner T, et al. Lessons learned from the deployment of a long-term autonomous robot as companion in physical therapy for older adults with dementia: a mixed methods study. The Eleventh ACM/IEEE International Conference on Human Robot Interaction, Christchurch, New Zealand; 2016. p. 27–34.
   Google Scholar
• Kwong EWY, Lai CKY, Spicciolato E, et al. Views of adults on shopping trolleys: implications for the development of a shopping trolley. Ergon Open J. 2010;3:32–37. doi: 10.2174/1875934301003010032
   Google Scholar
• Iwamura Y, Shiomi M, Kanda T, et al. Do elderly people prefer a conversational humanoid as a shopping assistant partner in supermarkets? ACM/IEEE Int. Conf. on Human–Robot Interaction (HRI2011), Lausanne, Switzerland; 2011 p. 449–456.
   Google Scholar
• Dautenhahn K, Walters M, Woods S, et al. How may i serve you? A robot companion approaching a seated person in a helping context. ACM/IEEE Int. Conf. on Human–Robot Interaction (HRI2006), Salt Lake City, UT, USA; 2006. p. 172–179.
   Google Scholar
• Kuzuoka H, Suzuki Y, Yamashita J, et al. Reconfiguring spatial formation arrangement by robot body orientation. ACM/IEEE Int. Conf. on Human–Robot Interaction (HRI2010), Osaka, Japan; 2010. p. 285–292.
   Google Scholar
• Mumm J, Mutlu B. Human–robot proxemics: physical and psychological distancing in human–robot interaction. ACM/IEEE Int. Conf. on Human–Robot Interaction (HRI2011), Lausanne, Switzerland; 2011. p. 331–338.
   Google Scholar
• Feil-Seifer D, Mataric M. Distance-based computational models for facilitating robot interaction with children. J Hum-Rob Interact. 2012;1:55–77. doi: 10.5898/JHRI.1.1.Feil-Seifer
   Google Scholar
• Kelley R, Tavakkoli A, King C, et al. Understanding human intentions via hidden markov models in autonomous mobile robots. Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction, Amsterdam, the Netherlands; 2008. p. 367–374.
   Google Scholar
• Satake S, Kanda T, Glas DF, et al. How to approach humans?: strategies for social robots to initiate interaction. ACM/IEEE Int. Conf. on Human–Robot Interaction (HRI2009), San Diego, CA, USA; 2009. p. 109–116.
   Google Scholar
• Yousuf MA, Kobayashi Y, Kuno Y, et al. How to move towards visitors: a model for museum guide robots to initiate conversation. IEEE Int. Symposium on Robot and Human Interactive Communication (RO-MAN2013), Gyeongju, Korea; 2013. p. 587–592.
   Google Scholar
• Pacchierotti E, Christensen HI, Jensfelt P. Evaluation of passing distance for social robots. IEEE Int. Symposium on Robot and Human Interactive Communication (RO-MAN2006), Hatfield, UK; 2006. p. 315–320.
   Google Scholar
• Sisbot EA, Marin-Urias LF, Alami R, et al. A human aware mobile robot motion planner. IEEE Trans Robot. 2007;23:874–883. doi: 10.1109/TRO.2007.904911
   Web of Science ®Google Scholar
• Gockley R, Forlizzi J, Simmons R. Natural person-following behavior for social robots. ACM/IEEE Int. Conf. on Human–Robot Interaction (HRI2007), Arlington, VA, USA; 2007. p. 17–24.
   Google Scholar
• Sviestins E, Mitsunaga N, Kanda T. Speed adaptation for a robot walking with a human. ACM/IEEE Int. Conf. on Human–Robot Interaction (HRI2007), Arlington, VA, USA; 2007. p. 349–356.
   Google Scholar
• Kobayashi Y, Kinpara Y, Takano E, et al. Robotic wheelchair moving with caregiver collaboratively depending on circumstances. Extended Abstracts on ACM Conference on Human Factors in Computing Systems (CHI2011), Vancouver, BC, Canada; 2011.
   Google Scholar
• Weizenbaum J. ELIZA—a computer program for the study of natural language communication between man and machine. Commun ACM. 1966;9(1):36–45. doi: 10.1145/365153.365168
   Web of Science ®Google Scholar
• Matsumoto S, Araki M. Scoring of response based on suitability of dialogue-act and content similarity. Proceedings of the 12th NTCIR Conference on Evaluation of Information Access Technologies, NII, Tokyo, Japan; 2016.
   Google Scholar
• Torrey C, Fussell SR, Kiesler S. How a robot should give advice. ACM/IEEE Int. Conf. on Human–Robot Interaction (HRI2013), Tokyo, Japan: National Museum of Emerging Science and Innovation (Miraikan); 2013. p. 275–282.
   Google Scholar
• Bickmore TW, Picard RW. Establishing and maintaining long-term human-computer relationships. ACM Trans Comput-Hum Interact (TOCHI2005). 2005;12:293–327. doi: 10.1145/1067860.1067867
   Google Scholar
• Lee MK, Forlizzi J, Kiesler S, et al. Personalization in HRI: a longitudinal field experiment. ACM/IEEE Int. Conf. on Human–Robot Interaction (HRI2012), Boston, MA, USA; 2012. p. 319–326.
   Google Scholar
• Leite I, Castellano G, Pereira A, et al. Empathic robots for long-term interaction: evaluating social presence, engagement and perceived support in children. Int J Soc Robot. 2014;6:329–341. doi: 10.1007/s12369-014-0227-1
   Web of Science ®Google Scholar
• Maas JF, Spexard T, Fritsch J, et al. Biron, what's the topic? A multi-modal topic tracker for improved human–robot interaction. ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication, Hatfield, UK; 2006. p. 26–32.
   Google Scholar
• Totsuka R, Satake S, Kanda T, et al. Is a robot a better walking partner if it associates utterances with visual scenes? ACM/IEEE Int. Conf. on Human–Robot Interaction (HRI2017), Aula der Wissenschaft, Vienna, Austria; 2017. p. 313–322.
   Google Scholar
• Maeda T, Takashima K, Kajimura Y, et al. A study of nonverbal cues and atmosphere in three-person conversation (Japanese). IEICE Technical Report, HCS. 2010;109(457):73–78.
   Google Scholar
• Google Cloud Speech Api, [Online]. Available from: https://cloud.google.com/speech/.
   Google Scholar
• Google Cloud Vision Api [Online]. Available from: https://cloud.google.com/vision/.
   Google Scholar
• Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.
   Web of Science ®Google Scholar
• Rehurek R, Sojka P. Software framework for topic modelling with large corpora. Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Valletta, Malta; 2010. p. 45–50.
   Google Scholar
• Hoffman M, Bach FR, Blei DM. Online learning for latent dirichlet allocation. Advances in Neural Information Processing Systems, Hyatt Regency, Vancouver, Canada; 2010. p. 856–864.
   Google Scholar
• Open Jtalk, [Online]. Available from: http://open-jtalk.sourceforge.net/.
   Google Scholar
• Walker MA, Litman DJ, Kamm CA, et al. PARADISE: a framework for evaluating spoken dialogue agents. Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics. Madrid, Spain: Association for Computational Linguistics; 1997. p. 271–280.
   Google Scholar
• Walker MA, Litman DJ, Kamm CA, et al. Evaluating spoken dialogue agents with PARADISE: two case studies. Comput Speech Lang. 1998;12(4):317–347. doi: 10.1006/csla.1998.0110
   Web of Science ®Google Scholar
• Kim MG, Oosterling I, Lourens T. Designing robot-assisted pivotal response training in game activity for children with autism. IEEE Int. Conf. on Systems, Man and Cybernetics (SMC2014), San Diego, CA, USA; 2014. p. 1101–1106.
   Google Scholar