Beyond R2D2

Abstract

It is likely that in the near future we will meet more and more robots that will perform tasks in social environments, such a museum or tourist site. However, design guidelines that inform the design of effective nonverbal behavior for robots are scarce. This is surprising since the behavior of robots in social environments can greatly affect the interaction between people and robots. A challenge in designing nonverbal behavior specifically for robots that do not resemble people in appearance (low-anthropomorphic robots) is that these robots often lack the abilities to imitate humanlike behavior correctly. Yet, low-anthropomorphic robots may use screens, projections, light cues or specific movements to communicate their intentions and motivations. To optimize the use of robot-specific modalities and morphology, we developed robot-optimized behavior. To understand the effects of both imitated humanlike behavior and robot-optimized behavior for low-anthropomorphic robots on peoples’ perception, several studies have been performed. First, we analyzed the effect of imitated humanlike guide behavior on low-anthropomorphic robots. These studies showed that humanlike behavior is preferred over random behavior, but that it is also more distracting. This means that humanlike behavior may not the best solution to design behavior for low-anthropomorphic robots. An important question now was what would be a promising alternative to imitating human behavior. In follow-up studies we compared the effect of imitated humanlike behavior for the robot to robot-optimized behavior. From these studies we learned that robot-optimized behavior is a good alternative for low-anthropomorphic robots instead of imitated humanlike behavior. Additionally, we developed and introduced DREAM, Data Reduction Event Analysis Method. This method allowed us to analyze video data of in-the-wild human-robot interaction in a fast and reliable manner. Therefore, the work presented in this thesis is a first step towards understanding 1) the effect of nonverbal behavior for low-anthropomorphic robots, 2) which design approaches can be effectively used to design this behavior and 3) how nonverbal robot behavior can be studied and evaluated in the wild.