Background and Previous Work

This chapter contributes to the growing domain of HRI studies “in the wild,” particularly those done in naturalistic, public settings in which diverse users have the opportunity to interact with robots in a voluntary and open-ended fashion. Such studies have been performed in museums (e.g. Nourbakhsh, Kunz, & Willeke, 2003; Yamazaki et al, 2009), malls (e.g. Kanda et al., 2009), university campuses (e.g. Gockley et al., 2005), city streets (e.g. Weiss et al. 2010), schools (e.g. Tanaka, Cicourel, & Movellan, 2007; Leite et al., 2012), and public areas of caregiving institutions (e.g. Chang & Sabanovic, 2014). The presented work also adds to the literature on “interaction patterns” in human-robot interaction, which involves the identification and description of repeating general patterns of interactive behavior between humans and robots that can be realized in a recognizable though unique manner in different contexts (Kahn et al., 2010a).

6.1.2.1 Studying Robots in Public Spaces

The majority of HRI studies in public spaces have focused on testing the social acceptability of robots in various environments, describing user reactions to robots in public spaces, and identifying design characteristics that support social acceptance and continued use. Straub et al. (2010) studied how people interacted with a Geminoid HI-1 android at a public café in both autonomous and telepresence modes, and found that participants ascribed humanistic traits to the robot independent of the operation mode. Ruiz-del-Solar et al. (2009) evaluated Bender, a robot that could speak and express anger, sadness, and happiness through facial expressions, in three different settings (a home, a school classroom, and a university building) and showed that people could generally understand the robot’s communicative attempts and were overall accepting of the robot. Studies with the “Roboceptionist,” a robot operating at the entrance to Carnegie Mellon University’s Robotics Institute since 2003, further showed that various interaction cues, including emotional responses and personalization, can affect people’s willingness to voluntarily engage and maintain interaction with a robot (Gockley et al., 2005). Evaluations of the ACE (autonomous city explorer) robot displayed that people are willing to communicate with and help a robot

dependent on the aid of passerby to plan a route to its final destination, suggesting that a social mode of navigation is viable (Weiss et al., 2010). Mutlu and Forlizzi’s (2008) ethnographic study of robot use in a hospital found that acceptance depended not on the robot’s characteristics alone, but on their relative fit into the social dynamics of the work environment.

There is a long history of using robots in museums, similarly to our own, as a way to study human-robot interaction in naturalistic settings and inform robot design. Museums can be seen as particularly advantageous settings for exploratory studies of HRI – people are there to learn and experience new things, so they may be more open to novel experiences with robots. Thrun et al.’s (1999) work with the museum guide robot Minerva acknowledged the importance of interactive capabilities as well as mobility and navigation for this application area. The five-year Mobot museum robot experience described by Nourbakhsh, Kunz, & Willeke (2003) yielded a series of requirements for successful human- robot interaction in a museum setting, including the importance of the physical appearance, movement, and social awareness of the robot as an enticement to interaction. They further identified multimodal interaction design, interactive tasks, and the need for the robot to follow human social norms (including giving negative reactions to behavior that is making it difficult for the robot to perform its job, such as crowding the robot) as ways to retain visitor attention. In developing a personal rover exhibit, Nourbakhsh et al (2005) showed the importance of reliability (failing rarely despite daily use and being easy to fix), autonomy (performing without staff intervention), and a self-explanatory user interface which allows people to interact with the robot without prior training or the need for explanation. Studies with robots in museums also often have some educational or informative purpose, so the transmission of information or meeting specific learning goals are important outcomes, aside from the success of the interaction.

6.1.2.3 Developing Interaction Patterns for Robots

Studies of robots in public spaces have also been used to develop and evaluate particular models of behavior for successful HRI. An ongoing project using the Robovie platform seeks to develop HRI

capabilities for day-to-day interactions with diverse users. Observations of interactions with customers, as well as multiple studies of user acceptance, indicate that robots were able to influence the shopping habits of customers and were evaluated positively (Kanda et al., 2009). In the museum context, Yamazaki et al (2009; 2012) used ethnographic fieldwork in museums to understand how tour guides perform their job and use the resulting behavioral models in the interaction design of robot guides, which they also test out in natural settings. These studies suggest that a particular model of robotic development, which uses observation of human-human behavior in naturalistic environments to develop models for robot behavior and evaluates those in HRI “in the wild,” can be useful for constructing robots for use in everyday settings.

Although HRI research commonly uses the Computers-As-Social-Actors (CASA) framework as a rationale for building interactive capabilities for robots by replicating those of humans, people do not always treat robots exactly like humans (Reeves & Nass 1996). Kahn et al. (2011) suggest robots inhabit a category between humans and machines in terms of the models that people use for interpreting and responding to their behavior. Our approach in this chapter has therefore been to explore how people behave towards an interactive robot in order to understand their initial reactions and develop further interaction capabilities for the robot. We are in particular interested in identifying repeated “interaction patterns” – “the glances, positionings, gestures” – and “sequences of behavior” that constitute face-to-face interaction (Kendon, 1990) between humans and robots as a foundation for future robot design. Such repeated behavioral sequences have been previously conceptualized as “design” or “interaction” patterns in HRI (a et al., 2008), which characterize “essential features of social interaction between humans and robots, specified abstractly enough such that many different instantiations of the pattern can be uniquely realized given different types of robots, purposes, and contexts of use” (Kahn et al., 2010a). Kahn et al have identified and used a variety of “interaction pattern” sequences, such as “Introduction” and “Walking together,” in their research (e.g. Kahn et al, 2012). They also suggested a framework for validating the existence of interaction patterns in HRI (Kahn et al., 2010b), which involves establishing the effectiveness of the patterns in facilitating HRI, the ability of the pattern to account for the data, and

establishing a sensible reason for naming the pattern. Peltason and Wrede (2010a) used interaction patterns extracted from a variety of human-robot interaction scenarios to assist in the development of algorithms for robot dialogue for real-world applications. This allowed them to combine abstract task states (such as task accepted or failed) with generalized dialogue acts (such as an apology), which could be adapted to different applications and situations. Finally, common conceptual interpretations (or schemas) of robots along with interaction patterns have been identified in qualitative studies of initial and continuing interactions between people and robots (Turkle, 2006, 2011).

Our work takes advantage of the museum context as a space in which people are able to create novel interactions without research influence. We explore the resulting behavioral patterns that emerge from such open-ended initial interactions between people and robots in order to identify common “interaction schemas” that a robot might need to recognize and participate in during initial interactions with people, and to inspire the development of appropriate responses that the robot could produce to successfully elicit further continued interaction.

6.2 Methods