State-Transition Modeling of Human-Robot Interaction for Easy Crowdsourced Robot Control

Robotic salespeople are often ignored by people due to their weak social presence, and thus have difficulty facilitating sales autonomously. However, for robots that are remotely controlled by humans, there is a need for experienced and trained operators. In this paper, we suggest crowdsourcing to allow general users on the internet to operate a robot remotely and facilitate customers' purchasing activities while flexibly responding to various situations through a user interface. To implement this system, we examined how our developed remote interface can improve a robot's social presence while being controlled by a human operator, including first-time users. Therefore, we investigated the typical flow of a customer-robot interaction that was effective for sales promotion, and modeled it as a state transition with automatic functions by accessing the robot's sensor information. Furthermore, we created a user interface based on the model and examined whether it was effective in a real environment. Finally, we conducted experiments to examine whether the user interface could be operated by an amateur user and enhance the robot's social presence. The results revealed that our model was able to improve the robot's social presence and facilitate customers' purchasing activity even when the operator was a first-time user.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:20

Enthalten in:

Sensors (Basel, Switzerland) - 20(2020), 22 vom: 15. Nov.

Sprache:

Englisch

Beteiligte Personen:

Iwasaki, Masaya [VerfasserIn]
Ikeda, Mizuki [VerfasserIn]
Kawamura, Tatsuyuki [VerfasserIn]
Nakanishi, Hideyuki [VerfasserIn]

Links:

Volltext

Themen:

Field trial
Journal Article
Multimodal conversation analysis
Robotic salesperson
Situation awareness

Anmerkungen:

Date Completed 06.04.2021

Date Revised 30.03.2024

published: Electronic

Citation Status MEDLINE

doi:

10.3390/s20226529

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM317703889