Is it possible to design robots of the future so that they can enhance people's creative endeavors? Forty-eight young adults were asked to produce their most creative ideas in a small Zen rock garden in a laboratory setting. Participants were randomly assigned to one of two conditions. In one condition, the robot Robovie (through a Wizard of Oz interface) encouraged participants to generate creative ideas (e.g., “Can you think of another way to do that?”), and pulled relevant images and video clip from the web for each participant to look at which could help spur the participant into more creative expressions. In a second condition, participants engaged in the same Zen rock garden task with the same core information, but through the modality of a self-paced PowerPoint presentation. Results showed that participants engaged in the creativity task longer and provided almost twice the number of creative expressions in the robot condition compared to the PowerPoint condition. Discussion focuses on a vision of social robotics coupled with advances in natural language processing to enhance the human creative mind and spirit.

Will people keep the secret of a socially compelling robot who shares, in confidence, a "personal" (robot) failing? Toward answering this question, 81 adults participated in a 20-minute interaction with (a) a humanoid robot (Robovie) interacting in a highly social way as a lab tour guide, and (b) with a human being interacting in the same highly social way. As a baseline comparison, participants also interacted with (c) a humanoid robot (Robovie) interacting in a more rudimentary social way. In each condition, the tour guide asks for the secret keeping behavior. Results showed that the majority of the participants (59%) kept the secret of the highly social robot, and did not tell the experimenter when asked directly, with the robot present. This percentage did not differ statistically from the percentage who kept the human's secret (67%). It did differ statistically when the robot engaged in the more rudimentary social interaction (11%). These results suggest that as humanoid robots become increasingly social in their interaction, that people will form increasingly intimate and trusting psychological relationships with them. Discussion focuses on design principles (how to engender psychological intimacy in human-robot interaction) and norms (whether it is even desirable to do so, and if so in what contexts).

This paper shows how humor can be used as an interaction pattern to help establish sociality in human-robot interaction. Drawing illustratively from our published research on people interacting with ATR's humanoid robot Robovie, we highlight four forms of humor that we successfully implemented: wit and the ice-breaker, corny joke, subtle humor, and dry humor and self-deprecation.

We offer three illustrative examples from one of our recent studies in HRI to suggest that it's possible for people to engage in mutual deliberation with a social robot. Each example illustrates discourse and argument, but ends through different means: Accepting the Robot as Arbitrator; Delegitimizing the Robot; and Agreeing to Disagree.

This research builds on the UbiComp vision of systems that do not do things for people but engage people in their computational environment so that people can do things for themselves better. In this investigation, we sought to make good on a proof-of-concept where people interact with a social robot whereby the robot helps people to be more humanly creative. Twenty seven participants interacted with ATR's humanoid robot Robovie (through a WoZ interface) in a creativity task. Results supported our proof of concept insofar as 100% of the participants generated creative ideas, and 63% incorporated the robot's ideas into their own ideas for their creative output. Of the participants who had the highest creativity scores, 83% incorporated the robot's ideas into their own. Discussion focuses on next steps toward building the Natural Language Processing system, and integrating the system into a more extensive networked UbiComp environment.

Robots will increasingly take on roles in our social lives where they can cause humans harm. When robots do so, will people hold robots morally accountable? To investigate this question, 40 undergraduate students individually engaged in a 15-minute interaction with ATR's humanoid robot, Robovie. The interaction culminated in a situation where Robovie incorrectly assessed the participant's performance in a game, and prevented the participant from winning a $20 prize. Each participant was then interviewed in a 50-minute session. Results showed that all of the participants engaged socially with Robovie, and many of them conceptualized Robovie as having mental/emotional and social attributes. Sixty-five percent of the participants attributed some level of moral accountability to Robovie. Statistically, participants held Robovie less accountable than they would a human, but more accountable than they would a vending machine. Results are discussed in terms of the New Ontological Category Hypothesis and robotic warfare.

This paper discusses converging evidence to support the hypothesis that personified robots and other embodied personified computational systems may represent a new ontological category, where ontology refers to basic categories of being, and ways of distinguishing them.

This conceptual paper broaches possibilities and limits of establishing robot persona in HRI.

In recent work, "interaction patterns" have been proposed as a means to characterize essential features of human-robot interaction. A problem arises, however, in knowing whether the interaction patterns generated are valid. The same problem arises when researchers in HRI propose other broad conceptualizations that seek to structure social interaction. In this paper, we address this general problem by distinguishing three ways of establishing the validity of interaction patterns. The first form of validity seeks to establish whether the conclusions about interaction patterns are warranted from the data. The second seeks to establish whether the interaction patterns account for the data. And the third seeks to provide sound reasons for the labels of the patterns themselves. Often these three forms of validity are confused in discussions about conceptual categories in HRI.

This conceptual paper broaches possibilities and limits of establishing psychological intimacy in HRI.

We propose that Christopher Alexander's idea of design patterns can benefit the emerging field of HRI. We first discuss four features of design patterns that appear particularly useful. For example, a pattern should be specified abstractly enough such that many different instantiations of the pattern can be uniquely realized in the solution to specific problems in context. Then, after describing our method for generating patterns, we offer and describe eight possible design patterns for sociality in human robot interaction: initial introduction, didactic communication, in motion together, personal interests and history, recovering from mistakes, reciprocal turn-taking in game context, physical intimacy, and claiming unfair treatment or wrongful harms. We also discuss the issue of validation of design patterns. If a design pattern program proves successful, it will provide HRI researchers with basic knowledge about human robot interaction, and save time through the reuse of patterns to achieve high levels of sociality.

This study investigated whether a robotic dog might aid in the social development of children with autism. Eleven children diagnosed with autism (ages 5-8) interacted with the robotic dog AIBO and, during a different period within the same experimental session, a simple mechanical toy dog (Kasha), which had no ability to detect or respond to its physical or social environment. Results showed that, in comparison to Kasha, the children spoke more words to AIBO, and more often engaged in three types of behavior with AIBO typical of children without autism: verbal engagement, reciprocal interaction, and authentic interaction. In addition, we found suggestive evidence (with p values ranging from .07 to .09) that the children interacted more with AIBO, and, while in the AIBO session, engaged in fewer autistic behaviors. Discussion focuses on why robotic animals might benefit children with autism.