We present a qualitative study with five healthcare experts spe-cialised in different types of touch practice to gain insight in how caring touch can be enacted. Through our analysis we focus onhow to transfer this learning into design considerations towards enacting caring touch from technologies. Despite the rapidly growing expectation for and design interest in touch from technologies intending to enhance care and well-being, the knowledge on how to design caring touch is still fragmented. How caring touch is enacted in inter-personal touch is under-explored and such expertise from healthcare practitioners has not been engaged from the perspective of HCI design research. We propose designers to consider caring as an experiential quality instead of a division between instrumental types of touch and caring types. We recommend when designing for a caring quality in technology-initiated touch that designers create a progression of touch with dynamic sensitivity and adapt the materiality of actuating devices to the plural dimensions of the body’s textures.

This study explores if, and how, the choices made regarding a robot's speaking voice and characteristic body sounds influence viewers' perceptions of its intent i.e., whether the robot's intention is positive or negative. The analysis focuses on robot representations and sounds in three films: "Robots" (2005) [1], "NextGen" (2018) [2], and "Love, Death, and Robots - Three Robots" (2019) [3]. In eight qualitative interviews, five parameters (tonality, intonation, volume, pitch, and speed) were used to understand robot sounds and the participant's perception of a robot's attitude and intentions. The study culminates in a set of recommendations and considerations for human-robot interaction designers to consider while sound coding for body, physiology, and movement.

Politeness is at the core of the common set of behavioral norms that regulate human communication and is therefore of significant interest in the design of Human-Robot Interactions. In this paper, we investigate how the politeness behaviors of a humanoid robot impact human decisions about where to join a group of two robots. We also evaluate the resulting impact on the perception of the robot's politeness. In a study involving 59 participants, the main (Pepper) robot in the group invited participants to join using six politeness behaviors derived from Brown and Levinson's politeness theory. It requests participants to join the group at the furthest side of the group which involves more effort to reach than a closer side that is also available to the participant but would ignore the request of the robot. We evaluated the robot's effectiveness in terms of persuasiveness, politeness, and clarity. We found that more direct and explicit politeness strategies derived from the theory have a higher level of success in persuading participants to join at the furthest side of the group. We also evaluated participants' adherence to social norms i.e. not walking through the center, or o-space, of the group when joining it. Our results showed that participants tended to adhere to social norms when joining at the furthest side by not walking through the center of the group of robots, even though they were informed that the robots were fully automated.

This article presents three studies where we probe aesthetics strategies of sound produced by movement sonification of a Pepper robot by mapping its movements to sound models. We developed two sets of sound models. The first set was made by two sound models, a sawtooth-based one and another based on feedback chains, for investigating how the perception of synthesized robot sounds would depend on their design complexity. We implemented the second set of sound models for probing the “materiality” of sound made by a robot in motion. This set consisted of a sound synthesis based on an engine highlighting the robot’s internal mechanisms, a metallic sound synthesis highlighting the robot’s typical appearance, and a whoosh sound synthesis highlighting the movement. We conducted three studies. The first study explores how the first set of sound models can influence the perception of expressive gestures of a Pepper robot through an online survey. In the second study, we carried out an experiment in a museum installation with a Pepper robot presented in two scenarios: (1) while welcoming patrons into a restaurant and (2) while providing information to visitors in a shopping center. Finally, in the third study, we conducted an online survey with stimuli similar to those used in the second study. Our findings suggest that participants preferred more complex sound models for the sonification of robot movements. Concerning the materiality, participants liked better subtle sounds that blend well with the ambient sound (i.e., less distracting) and soundscapes in which sound sources can be identified. Also, sound preferences varied depending on the context in which participants experienced the robot-generated sounds (e.g., as a live museum installation vs. an online display).

Social robots have a recognizable physical appearance, a distinct voice, and interact with users in specific contexts. Previous research has suggested a `matching hypothesis', which seeks to rationalise how people judge a robot's appropriateness for a task by its appearance. Other research has extended this to cover combinations of robot voices and appearances. In this paper, we examine the missing connection between robot voice, robot appearance, and deployment context. In so doing, we asked participants to match a robot image to a voice within a defined interaction context. We selected widely available social robots, identified task contexts they are used in, and manipulated the voices in terms of gender, naturalness, and accent. We found that the task context mediates the `matching hypothesis'. People consistently selected a robot based on a vocal feature for a certain context, and a different robot based on the same vocal feature for another context. We suggest that robot voice design should take advantage of current technology that enables the creation and tuning of custom voices. They are a flexible tool to increase perception of appropriateness, which has a positive influence on Human-Robot Interaction.