This paper examines the advantages and disadvantages of combining Human-Like and Machine-Like behaviors for a robot taking a shared elevator with a bystander as part of an office delivery service scenario. We present findings of an in-person wizard-of-oz experiment that builds on and implements behavior policies developed in a previous study. In this experiment, we found that the combination of Machine-Like and Human-Like behaviors was perceived as better than Human-Like behaviors alone. We discuss possible reasons and point to key capabilities that a socially competent robot should have to achieve better Human-Like behaviors in order to seamlessly negotiate a social encounter with bystanders in a shared elevator or similar scenario. We found that establishing and maintaining a shared transactional space is one of these key requirements.

When people engage in urban exploration, the tool they are most likely to use today is a mobile phone. In this paper, we present observations of users’ “home” and “away” conducted to refine our understanding of situational Point-of-Interest (POI) needs. Our findings suggest three distinct categories of situations in which users seek POI information: On-the-spot, Refining plans, and Moments of boredom. Based on the similarities and differences of these three situations in five observed underlying constraints – distance of interest, engagement threshold, ambiguity of the search, profile matching, and other imperative constraints, we derive implications for designing and ranking POIs for a Situational Recommender. To further access our concept, we designed and prototyped Situational Recommender by providing an interactional representation of the situation, and ran a Wizard-of-Oz concept validation study. Our results suggest that participants understood the concept without much effort and appreciated its usefulness.

In this paper, we present our ongoing research on socially acceptable robot navigation for an indoor elevator sharing scenario. Informed by naturalistic observations of human elevator use, we discuss the social nuances involved in a seemingly simple activity like taking an elevator and the challenges and limitations of modeling robot behaviors based on a full human-like approach. We propose the principle of machine-like for the design of robot behavior policies that effectively accomplish tasks without being disruptive to the routines of people sharing the elevator with the robots. We explored this approach in a bodystorming session and conducted a preliminary evaluation of the resulting considerations through an online user study. Partic-ipants differentiated robots from humans for issues of proxemics and priority, and machine-like behaviors were preferred over human-like behaviors. We present our findings and discuss the advantages and limitations identified for both approaches for designing socially acceptable navigation behaviors.