Signaling trouble in robot-to-group interaction.emerging visitor dynamics with a museum guide robot.

Gehle, Raphaela; Pitsch, Karola; Wrede, Sebastian

doi:10.1145/2658861.2658887

Cited by 15 publications

(11 citation statements)

References 17 publications

(25 reference statements)

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“…Many real-world experiments have already attempted to study natural interactions between robots and humans. For example, experiments have been conducted in museums (Bennewitz et al, 2005;Kuno et al, 2007;Yamazaki et al, 2008;Gehle et al, 2014) and a classroom (Tanaka et al, 2015). In addition, several experiments have employed robots as salespeople for different purposes (Lee et al, 2012;Nakagawa et al, 2013;Niemelä et al, 2017a,b).…”

Section: Related Work Field Trialsmentioning

confidence: 99%

“…Many studies have considered ways to utilize these types of robots. For example, experiments have been conducted on the use of guidance robots in museums (Shiomi et al, 2006;Lee et al, 2010;Tanaka et al, 2015); further, robots have been adopted for educational purposes (Gehle et al, 2014). Additionally, much research has focused on employing robotic salespeople in real-world shops.…”

Section: Introductionmentioning

confidence: 99%

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“That Robot Stared Back at Me!”: Demonstrating Perceptual Ability Is Key to Successful Human–Robot Interactions

et al. 2019

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Communication robots, such as robotic salespeople and guide robots, are increasingly becoming involved in various aspects of people's everyday lives. However, it is still unclear what types of robot behavior are most effective for such purposes. In this research, we focused on a robotic salesperson. We believe that people often ignore what such robots have to say owing to their weak social presence. Thus, these robots must behave in ways that attract attention encouraging people to nod or reply when the robots speak. In order to identify suitable behaviors, we conducted two experiments. First, we conducted a field experiment in a shop in a traditional Kyoto shopping street to observe customers' real-world interactions with a robotic salesperson. Here, we found that the first impression given by the robot had a crucial influence on its subsequent conversations with most customer groups and that it was important for the robot to indicate it could understand how much attention customers were paying to the robot in the early stages of its interactions if it was to persuade customers to respond to what it said. Although the field experiment enabled us to observe natural interactions, it also included many external factors. In order to validate some of our findings without the involving these factors, we further conducted a laboratory experiment to investigate whether having the robot look back at the participants when they looked at it increased their perception that the robot was aware of their actions. These results supported the findings of the field experiment. Thus, we can conclude that demonstrating that a robot can recognize and respond to human behavior is important if it is to engage with people and persuade them to nod and reply to its comments.

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Section: Related Work Field Trialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“That Robot Stared Back at Me!”: Demonstrating Perceptual Ability Is Key to Successful Human–Robot Interactions

et al. 2019

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“…Therefore, these robots seem to be suitable to perform social tasks in everyday environments, such as schools and museums. Examples of such robots are: NAO (e.g., used in References [8,29,51]) or ASIMO (e.g., used in References [56,67]).…”

Section: Design Of Humanoid and Non-human-like Robots And Their Behaviormentioning

confidence: 99%

Beyond R2D2

Ludden

Evers

2019

J. Hum.-Robot Interact.

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Robots are expected to enter the everyday lives of people to entertain, educate, or support them. It is therefore important that people can intuitively understand the behavior of robots. Oftentimes, the behavior of people is used as a model because of its familiarity. However, it is as yet unclear what the best approach is to design interaction behaviors for non-humanoid robots. In this article, we explore two different approaches toward designing behavior for a service robot. The first concerns the commonly used approach of copying human behavior as closely as possible to the robot (human-translated). The second approach was inspired by product design methods. The design of the robot's behavior was optimized for the robot's interaction capabilities and hardware modalities (robot-optimized). To evaluate people's responses to the two behavior sets for a tour guide robot, an online video study (N = 204) and a two-day in-the-wild study (N > 600) were performed. Results showed that participants responded slightly more positive to robot-optimized behavior and paid attention to robot-optimized behavior for longer. However, participants remembered more details when the robot showed human-translated behavior. Together, the studies show that it is sometimes better for non-humanoid robots to have robot-optimized behaviors rather than human-translated behaviors. CCS Concepts: • Human-centered computing → HCI design and evaluation methods;

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“…Different types of causes for clarification used in (Schlangen, 2004, Ex. (12) Other-initiated self-repair when the machine is the trouble-speaker is explored in (Gehle et al, 2014). Based on a corpus of video-recorded human-robot-interactions in a museum, the authors analyse interactional resources used by museum visitors to signal troubles in understanding robot's talk and dealing with misunderstandings.…”

Section: Repair In Conversational Agentsmentioning

confidence: 99%

A data-driven model of explanations for a chatbot that helps to practice conversation in a foreign language

Höhn¹

2017

Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

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This article describes a model of otherinitiated self-repair for a chatbot that helps to practice conversation in a foreign language. The model was developed using a corpus of instant messaging conversations between German native and non-native speakers. Conversation Analysis helped to create computational models from a small number of examples. The model has been validated in an AIML-based chatbot. Unlike typical retrieval-based dialogue systems, the explanations are generated at run-time from a linguistic database.

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Signaling trouble in robot-to-group interaction.emerging visitor dynamics with a museum guide robot.

Cited by 15 publications

References 17 publications

“That Robot Stared Back at Me!”: Demonstrating Perceptual Ability Is Key to Successful Human–Robot Interactions

“That Robot Stared Back at Me!”: Demonstrating Perceptual Ability Is Key to Successful Human–Robot Interactions

Beyond R2D2

A data-driven model of explanations for a chatbot that helps to practice conversation in a foreign language

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