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2017
Park, C.; Kim, J.; Kang, J. (2017) : Turn-taking intention recognition using multimodal cues in social human-robot interaction: 2017 17th International Conference on Control, Automation and Systems (ICCAS): Jeju, Korea: IEEE, S. 1300-1302

DOI: https://doi.org/10.23919/ICCAS.2017.8204407

Abstract: Turn-taking is an essential social skill for human communication. The robot needs to recognize the end of turn for timely response to the user with little delay in human-robot interaction. In this paper, we propose a turn-taking intention recognition system that determine the timing of turn-taking using multimodal cues in social Human-Robot Interaction (sHRI). In order to evaluate the turn-taking intention recognition system, we collect multimodal data set and conducted experiments. To that end, we designed a human-robot interaction scenario including turn-taking and conducted an experiment with 30 participants using the humanoid robot NAO. In experiments, we validate recognition models trained multimodal dataset by machine learning methods.

Keywords: Encoding, essential social skill, Floors, human communication, humanoid robot NAO, Humanoid Robots, human-robot interaction, ieee xplore, Künstliche Intelligenz, learning (artificial intelligence), Lips, machine learning methods, multimodal cues, multimodal dataset, Robots, sHRI, social human-robot interaction, Social robot, speech, timing, Turn-Taking, turn-taking intention recognition system
2011
Häring, M.; Bee, N.; André, E. (2011) : Creation and Evaluation of emotion expression with body movement, sound and eye color for humanoid robots: 2011 RO-MAN: 2011 RO-MAN: Georgia, USA: IEEE, S. 204-209

DOI: https://doi.org/10.1109/ROMAN.2011.6005263

Abstract: The ability to display emotions is a key feature in human communication and also for robots that are expected to interact with humans in social environments. For expressions based on Body Movement and other signals than facial expressions, like Sound, no common grounds have been established so far. Based on psychological research on human expression of emotions and perception of emotional stimuli we created eight different expressional designs for the emotions Anger, Sadness, Fear and Joy, consisting of Body Movements, Sounds and Eye Colors. In a large pre-test we evaluated the recognition ratios for the different expressional designs. In our main experiment we separated the expressional designs into their single cues (Body Movement, Sound, Eye Color) and evaluated their expressivity. The detailed view at the perception of our expressional cues, allowed us to evaluate the appropriateness of the stimuli, check our implementations for flaws and build a basis for systematical revision. Our analysis revealed that almost all Body Movements were appropriate for their target emotion and that some of our Sounds need a revision. Eye Colors could be identified as an unreliable component for emotional expression.

Keywords: Analysis of Variance, Angemessen(heit) (von Technik), anger emotion, Body Movement, Color, emotion expression, eye color, fear emotion, human communication, Humanoid Robots, Humans, ieee xplore, Image color analysis, joy emotion, Manipulators, Mobile robots, Particle measurements, sadness emotion

2017

2011

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