A model of the user's proximity for bayesian inference

E. Torta; R.H. Cuijpers; J.F. Juola

doi:10.1145/1957656.1957767

A model of the user's proximity for bayesian inference

E. Torta
, R.H. Cuijpers
, J.F. Juola

Human Technology Interaction

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

8 Citations (Scopus)

197 Downloads (Pure)

Abstract

Embodied nonverbal cues are fundamental for regulating human-human social iteractions. The physical embodiment of robots makes it likely that they will have to exhibit appropriate nonverbal interactive behaviors. In this paper we propose a model of the user's proximity based on a superposition of quasi-Gaussian probability distributions which allows to express findings from HRI trials regarding distances and direction of approach in a human-robot interaction scenario. The way the model is formulated is suitable for well-established Bayesian filtering techniques, and thus the inference of the preferred distance and direction of approach in a human robot interaction scenario can be regarded as a state estimation problem. Results derived from simulations show the effectiveness of the inference process.

Original language	English
Title of host publication	Proceedings of the 6th International Conference on Human-Robot Interaction (HRI 2011), March 6-9, 2011
Editors	A. Billard, P. Kahn, J.A, Adams, G. Trafton
Place of Publication	New York
Publisher	Association for Computing Machinery, Inc.
Pages	273-274
ISBN (Print)	978-1-4503-0561-7
DOIs	https://doi.org/10.1145/1957656.1957767
Publication status	Published - 2011
Event	6th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2011 - Lausanne, Switzerland Duration: 6 Mar 2011 → 9 Mar 2011 Conference number: 6

Conference

Conference	6th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2011
Abbreviated title	HRI 2016
Country/Territory	Switzerland
City	Lausanne
Period	6/03/11 → 9/03/11

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10.1145/1957656.1957767

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title = "A model of the user's proximity for bayesian inference",

abstract = "Embodied nonverbal cues are fundamental for regulating human-human social iteractions. The physical embodiment of robots makes it likely that they will have to exhibit appropriate nonverbal interactive behaviors. In this paper we propose a model of the user's proximity based on a superposition of quasi-Gaussian probability distributions which allows to express findings from HRI trials regarding distances and direction of approach in a human-robot interaction scenario. The way the model is formulated is suitable for well-established Bayesian filtering techniques, and thus the inference of the preferred distance and direction of approach in a human robot interaction scenario can be regarded as a state estimation problem. Results derived from simulations show the effectiveness of the inference process.",

author = "E. Torta and R.H. Cuijpers and J.F. Juola",

year = "2011",

doi = "10.1145/1957656.1957767",

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editor = "A. Billard and P. Kahn and J.A, Adams and G. Trafton",

booktitle = "Proceedings of the 6th International Conference on Human-Robot Interaction (HRI 2011), March 6-9, 2011",

publisher = "Association for Computing Machinery, Inc.",

address = "United States",

note = "6th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2011, HRI 2016 ; Conference date: 06-03-2011 Through 09-03-2011",

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Torta, E , Cuijpers, RH & Juola, JF 2011, A model of the user's proximity for bayesian inference. in A Billard, P Kahn, JA Adams & G Trafton (eds), Proceedings of the 6th International Conference on Human-Robot Interaction (HRI 2011), March 6-9, 2011. Association for Computing Machinery, Inc., New York, pp. 273-274, 6th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2011, Lausanne, Switzerland, 6/03/11. https://doi.org/10.1145/1957656.1957767

A model of the user's proximity for bayesian inference. / Torta, E.; Cuijpers, R.H.; Juola, J.F.
Proceedings of the 6th International Conference on Human-Robot Interaction (HRI 2011), March 6-9, 2011. ed. / A. Billard; P. Kahn; J.A, Adams; G. Trafton. New York: Association for Computing Machinery, Inc., 2011. p. 273-274.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N2 - Embodied nonverbal cues are fundamental for regulating human-human social iteractions. The physical embodiment of robots makes it likely that they will have to exhibit appropriate nonverbal interactive behaviors. In this paper we propose a model of the user's proximity based on a superposition of quasi-Gaussian probability distributions which allows to express findings from HRI trials regarding distances and direction of approach in a human-robot interaction scenario. The way the model is formulated is suitable for well-established Bayesian filtering techniques, and thus the inference of the preferred distance and direction of approach in a human robot interaction scenario can be regarded as a state estimation problem. Results derived from simulations show the effectiveness of the inference process.

AB - Embodied nonverbal cues are fundamental for regulating human-human social iteractions. The physical embodiment of robots makes it likely that they will have to exhibit appropriate nonverbal interactive behaviors. In this paper we propose a model of the user's proximity based on a superposition of quasi-Gaussian probability distributions which allows to express findings from HRI trials regarding distances and direction of approach in a human-robot interaction scenario. The way the model is formulated is suitable for well-established Bayesian filtering techniques, and thus the inference of the preferred distance and direction of approach in a human robot interaction scenario can be regarded as a state estimation problem. Results derived from simulations show the effectiveness of the inference process.

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M3 - Conference contribution

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T2 - 6th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2011

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ER -

A model of the user's proximity for bayesian inference

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