Favorable assessments of social robots are addressed in several research and development attempts because positive attitudes and intentions towards technology are regarded as a necessary prerequisite for usage. To predict a favorable evaluation, it is inevitable to understand the appraisal process and determine crucial variables that affect the evaluative and behavioral consequences of HRI. Robotic morphology has been identified as one of these variables. In the present work we expand previous work by demonstrating that capability attributions associated with robots’ morphological features explain variations in evaluations. Based on two large picture-based online studies (Study 1,
n
=673; Study 2,
n
=586) we show that robots with similar morphological features (e.g., robots with arms and grippers) can be clustered along their assigned capabilities, and that these capabilities (e.g., to manipulate objects) explain evaluations of the robots in terms of acceptance and social attributes (i.e., warmth, competence, discomfort). We discuss whether these initial assessments are relevant to live interactions and how our results can inform robot design.
Communication is a central component in social human–robot interaction that needs to be planned and designed prior to the actual communicative act. We therefore propose a pragmatic, linear view of communication design for social robots that corresponds to a sender–receiver perspective. Our framework is based on Lasswell’s 5Ws of mass communication: Who, says what, in which channel, to whom, with what effect. We extend and adapt this model to communication in HRI. In addition, we point out that, besides the predefined communicative acts of a robot, other characteristics, such as a robot’s morphology, can also have an impact on humans, since humans tend to assign meaning to every cue in robots’ behavior and appearance. We illustrate the application of the extended framework to three different studies on human–robot communication to demonstrate the incremental value as it supports a systematic evaluation and the identification of similarities, differences, and research gaps. The framework therefore offers the opportunity for meta-analyses of existing research and additionally draws the path for future robust research designs for studying human–robot communication.
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