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Sociable Dining Table: Meaning Acquisition Exploration in Knock-Based Proto-Communication

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Abstract

In order to build social robots that can coexist with human beings, it is necessary to understand the mechanisms of how communication protocols are developed in human–robot interactions. Our main goal is to explore how a communication protocol can be established incrementally between a human and our minimally designed robot which is called sociable dining table (SDT). SDT integrates a dish robot put on the table and behaves according to the knocks that a human emits. To achieve our goal, we conducted two experiments: a human–human experiment (Wizard-of-Oz) and a human–robot interaction (HRI) experiment. The aim of the first experiment was to explore how people build a protocol of communication. Based on the first experiment, we suggested an actor-critic architecture that simulated in an open-ended way the adaptive behavior which we determine in the first experiment. After that, we demonstrated in the HRI experiment how our actor-critic architecture enabled the adaptation to individual preferences in order to obtain a personalized protocol of communication.

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Notes

  1. We estimated this period based on a previous pilot study.

  2. Continuous-knocking was related to the presence of contiguous disagreements about the shared rules, and we defined a disagreement state in the Sect. 4.3.2.

  3. As an example, the percentage of agreement states \(=\) number of agreement states/(number of agreement states \(+\) number of disagreement states).

  4. It consists of choosing the most frequent behavior that was previously associated to the same number of knocks previously received and led to an agreement state; e.g.,: choosing the left behavior when we have 3 knocks led most probably to an agreement state while choosing back may have led to a disagreement because it has led less frequently used for an agreement state based on the previous interactions.

  5. Real time: Because the communication patterns emerge in a sequential fashion and we remarked that communication protocols were personalized to the pairs, any attempt to integrate a batch learning method to the robot’s architecture could not succeed in establishing the same customized protocols that we had seen in the first experiment, and that it is why we needed an online machine learning method. An online machine learning method gathers the data and learns incrementally.

  6. We suppose that a knocking pattern that involves a number of knocks superior than 4 knocks.

  7. We calculated approximately the value based on a pilot study.

  8. Here we had actually 3 dimensions for each of the trials F1, F2 and F3 to reach 100 %, but the highest possible representation in 2 dimensions consisted of choosing the F1 and F2 more so than either F1 and F3 or F2 and F3.

  9. The percentages were calculated based on the same formula used during the experiment 1.

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Acknowledgments

This research is supported by Grant-in-Aid for scientific research of KIBAN-B (26280102) from the Japan Society for the Promotion of science (JSPS).

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Authors and Affiliations

  1. Interaction and Communication Design Lab., Toyohashi University of Technology, 1-1 Hibarigaoka, Tempaku, Toyohashi, Aichi-ken, Japan

    Khaoula Youssef, Takahiro Asano, P. Ravindra S. De Silva & Michio Okada

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  1. Khaoula Youssef
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  2. Takahiro Asano
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  3. P. Ravindra S. De Silva
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  4. Michio Okada
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Correspondence to Khaoula Youssef.

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Youssef, K., Asano, T., De Silva, P.R.S. et al. Sociable Dining Table: Meaning Acquisition Exploration in Knock-Based Proto-Communication. Int J of Soc Robotics 8, 67–84 (2016). https://doi.org/10.1007/s12369-015-0314-y

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