Towards a typology of meaningful signals and cues in social robotics

Hegel, Frank; Gieselmann, Sebastian; Peters, Annika; Holthaus, Patrick; Wrede, Britta

doi:10.1109/roman.2011.6005246

Cited by 37 publications

(28 citation statements)

References 17 publications

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“…Research has shown that when approaching each other, people exchange social signals using non-verbal communication [7]. Also their appearance is a signal that provides information to the other person [5]. While robot designers can control some of these latter signals by ways of morphological design, robots fall short in employing subtle (nonverbal) signals, such as short glances or gestures, due to technical limitations.…”

Section: Introductionmentioning

confidence: 99%

Sound over matter

Joosse

Lohse

Evers

2014

Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction

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In our previous work we introduced functional noise as a modality for robots to communicate intent [6]. In this follow-up experiment, we replicated the first study with a robot which was taller in order to find out if the same results would apply to a tall vs. a short robot. Our results show a similar trend: a robot using functional noise is perceived more positively compared with a robot that does not.

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Section: Introductionmentioning

confidence: 99%

Sound over matter

Joosse

Lohse

Evers

2014

Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction

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“…Social intelligence then begins with the perception of social cues and signals (operationalized in the next section). While some researchers are studying how to embody cues to project certain signals 10,26 , less attention has been given to the various sensor systems that may be implemented to support social interfacing in robotics.…”

Section: Natural and Artificial Social-cognitive Systems Are Embodiedmentioning

confidence: 99%

“…An initial attempt at such a taxonomy, however, has been produced with respects to implementations within social robotics, providing a distinction between natural cues/signals (i.e., those present in humans that can be mimicked by robots) and artificial cues/signals (i.e., those that only an artificial agent can express, such as beeps, LED lights, etc.) 26 . The scope of application within SSP falls within two general categories: analysis and synthesis.…”

Section: Social Cues and Signalsmentioning

confidence: 99%

An interdisciplinary taxonomy of social cues and signals in the service of engineering robotic social intelligence

et al. 2014

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Understanding intentions is a complex social-cognitive task for humans, let alone machines. In this paper we discuss how the developing field of Social Signal Processing, and assessing social cues to interpret social signals, may help to develop a foundation for robotic social intelligence. We describe a taxonomy to further R&D in HRI and facilitate natural interactions between humans and robots. This is based upon an interdisciplinary framework developed to integrate: (1) the sensors used for detecting social cues, (2) the parameters for differentiating and classifying differing levels of those cues, and (3) how sets of social cues indicate specific social signals. This is necessarily an iterative process, as technologies improve and social science researchers better understand the complex interactions of vast quantities of social cue combinations. As such, the goal of this paper is to advance a taxonomy of this nature to further stimulate interdisciplinary collaboration in the development of advanced social intelligence that mutually informs areas of robotic perception and intelligence.

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“…Typically gestures have been studied in the human-computer interaction field, but having an intelligent, pysically present interaction partner means that gesture recognition should be paired with gesture generation and it should be learned (and taught) both ways. In addition, many communicative gestures are not so much intentionally, but they do follow some (social) conventions [42]. Many of these (social) signals and cues play a vital role in interaction and should become part of any learning setting.…”

Section: From Simple Behaviors To Generalized Skillsmentioning

confidence: 99%

Machine learning for interactive systems and robots

Cuayáhuitl

Otterlo

Dethlefs

et al. 2013

Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Co

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Research on interactive systems and robots, i.e. interactive machines that perceive, act and communicate, has applied a multitude of different machine learning frameworks in recent years, many of which are based on a form of reinforcement learning (RL). In this paper, we will provide a brief introduction to the application of machine learning techniques in interactive learning systems. We identify several dimensions along which interactive learning systems can be analyzed. We argue that while many applications of interactive machines seem different at first sight, sufficient commonalities exist in terms of the challenges faced. By identifying these commonalities between (learning) approaches, and by taking interdisciplinary approaches towards the challenges, we anticipate more effective design and development of sophisticated machines that perceive, act and communicate in complex, dynamic and uncertain environments.

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Towards a typology of meaningful signals and cues in social robotics

Cited by 37 publications

References 17 publications

Sound over matter

Sound over matter

An interdisciplinary taxonomy of social cues and signals in the service of engineering robotic social intelligence

Machine learning for interactive systems and robots

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