This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...) is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty. (shrink)

Human–robot interactions are often affected by error situations that are caused by either the robot or the human. Therefore, robots would profit from the ability to recognize when error situations occur. We investigated the verbal and non-verbal social signals that humans show when error situations occur in human–robot interaction experiments. For that, we analyzed 201 videos of five human–robot interaction user studies with varying tasks from four independent projects. The analysis shows that there are two types of error situations: social (...) norm violations and technical failures. Social norm violations are situations in which the robot does not adhere to the underlying social script of the interaction. Technical failures are caused by technical shortcomings of the robot. The results of the video analysis show that the study participants use many head movements and very few gestures, but they often smile, when in an error situation with the robot. The results suggest that participants do not see the robot as a social interaction partner with comparable communication skills. Our findings have implications for builders and evaluators of human–robot interaction systems. The builders need to consider including modules for recognition and classification of head movements to the robot input channels. The evaluators need to make sure that the presence of an experimenter does not skew the results of their user studies. (shrink)

A mobile service robot performing a task for its user might not be able to accomplish its mission without help from other people present in the shared environment. In previous research, collaborative control has been studied as an interactive mode of operation with a robot, compensating for its limitations in autonomy. However, few studies of robots requesting assistance by detecting potential collaborators, directing its attention to them, addressing them, and finally obtaining help from them, have previously been performed in real-world (...) use contexts. This study focuses on a fetch-and-carry robot, Cero, which has been designed to operate in an office environment as an aid for motion-impaired users. During its missions, the robot sometimes needs help with loading or unloading an object. The main question for the study was: under what conditions are people willing to help when requested to do so by the robot? We were particularly interested in bystanders, i.e. people who happened to be in the environment but who did not “have any official business” with the robot. To answer these questions and to provide a better understanding of human–robot help-seeking situations, we conducted an experimental study in which subjects who had not encountered our service robot before were requested to assist it with a task. The results of the study confirm that bystanders can to some degree be expected to help in robot missions, but that their willingness to help the robot depends on the situation and state of occupation that people are in when requested to interact with and assist the robot. (shrink)

A change of gaze behavior at a small mistake moment is a natural response that reveals our own mistakes and suggests an apology to others with whom we are working or interacting. In this paper we investigate how robot gaze behaviors at small mistake moments change the impressions of others. To prepare gaze behaviors for a robot, first, we identified by questionnaires how human gaze behaviors change in such situations and extracted three kinds: looking at the other, looking down, and (...) looking away. We prepared each gaze behavior, added a no-gaze behavior, and investigated how a robot’s gaze behavior at a small mistake moment changes the impressions of the interacting people in a simple cooperative task. Experiment results show that the ‘looking at the other’ gaze behavior outperforms the other gaze behaviors and indicates the degrees of perceived apologeticness and friendliness. Keywords: Communication robots; gaze; mistake; mitigation. (shrink)