The potential of spatial sound to immerse, guide, and affect humans is well-known, but has so far received little attention in the field of human-robot interaction. In this paper, we therefore explore how a robot emitting spatial sound affects a human’s impressions and behavior. Following a Research-through-Design (RtD) approach, we created two immersive robot sound designs which are emitted across the robot’s body and through speakers in the environment. In an evaluation study, participants interacted with the robot and shared their impressions through semi-structured interviews. Reactions showed that spatial robot sound had a notable effect on participant behavior and impressions, influencing, among others, how much attention they paid to the physical robot and how much animacy and agency they attributed to it. We report on our insights into how spatial sound may provide social robots with new ways to inform and engage the humans around them.

INTRODUCTION: Sound is an important interaction modality and a large part of human interaction happens in the aural domain. While research in Human-Robot Interaction (HRI) has long explored spoken language for interacting with humans, sound as a broader-and, to a significant degree, nonlexical (i.e., without words)-medium has been given comparably less attention. Yet, the range of sounds that robots can produce is vast, encompassing, among others, mechanical noise, music, and utterances that mimic human and animal vocalizations with varying degrees of realism. The sound of a robot's machinery can shape our perceptions and expectations [11, 17], music serves as a medium for robots to engage and communicate [18], and shared musical experiences can strengthen the bond between humans and robots [5]. Sonifications may enhance the legibility of movement and gestures [4, 7, 15] and beep sounds may be used to communicate emotions [2, 14]. Getting closer to themargins of language, robotsmay take inspiration from non-lexical fillers such as "uh" [13, 16] and backchannels such as "mhmm" [8, 12]. More generally, pitch, intensity, and other human prosodic variations may be drawn on in robot sound design [3, 10]. The information that can be extracted from sound in a robot's environment is equally rich. Beyond the recognition of semantic content, robots use, for example, sound source localization to gain a better understanding of their environment [9], or analyze a human's voice timbre and tone to distinguish speakers [6] and detect emotion [1].

Can we influence how a robot is perceived by designing the sound of its movement? Drawing from practices in film sound, we overlaid a video depicting a robot's movement routine with three types of artificial movement sound. In a between-subject study design, participants saw either one of the three designs or a quiet control condition and rated the robot's movement quality, safety, capability, and attractiveness. We found that, compared to our control, the sound designs both increased and decreased perceived movement quality. Coupling the same robotic movement with different sounds lead to the motions being rated as more or less precise, elegant, jerky, or uncontrolled, among others. We further found that the sound conditions decreased perceived safety, and did not affect perceived capability and attractiveness. More unrealistic sound conditions led to larger differences in ratings, while the subtle addition of harmonic material was not rated differently to the control condition in any of the measures. Based on these findings, we discuss the challenges and opportunities regarding the use of artificial movement sound as an implicit channel of communication that may eventually be able to selectively target specific characteristics, helping designers in creating more refined and nuanced human-robot interactions.

This paper aims to link Jean Louis Baudry's theory of cinematographic apparatus, and in particular his “dream analogy”, to the way we interpret experiential states within a Virtual Reality environment. Analogous elements that constitute and assist in the reading of the “dream analogy” will be presented as threads for consideration of the relevance and validity of such theory to Virtual Reality. The paper extends research previously undertaken by the author which introduced and linked the theory of the apparatus to electronic media art and in particular to interactive installations and responsive environments.

Experiments were conducted to quantify the direction of robot approach that a pair of seated people find most comfortable. Three maximally-different seating configurations and eight directions of robot approach were considered. The data were analysed using Rayleigh's test of uniformity, a directional statistics method. Results from 140 participants showed robot approach directions that minimise participant discomfort align spatially with regions that allow good sight of the robot by both people and are centred on the pair's largest unoccupied area of p-space.

Latest research in robotics is driven by a desire to develop systems that would allow humans to interact with robots in natural and intuitive ways. In this paper we investigate peoples' initial believes, expectations and ascribed mental capabilities of a robot when they encounter it for the first time and how these change after interacting with the robot. The differences between two robots, which differ in morphology and behavior, are compared.

This study focuses on differences and similarities of perception of a small humanoid robot between Japanese and Australian participants. Two conditions were investigated: participants actively interacting with the robot and bystanders observing the interaction. Experimental results suggested that, while the robot was perceived as highly likeable, Japanese participants rated the robot higher for animacy, intelligence and safety. Furthermore, passive observations of the interaction (rather than active interaction) resulted in higher ratings by Japanese participants for anthropomorphism, animacy, intelligence and safety. The findings are discussed in terms of cultural background and robot perception.

The study of human-robot interaction commonly occurs in a laboratory environment in an endeavour to control the experimental conditions. Although convenient for the experimenters, a laboratory setting can constrain and therefore alter a participant's behaviour. In this paper we introduce the method of `open experimentation' in which human-robot interaction studies take place outside the laboratory in a public social environment. Results from over 4,300 participants interacting with a humanoid robot, Diamandini, demonstrate that the general population preferred to observe the robot from a social distance, while children were comfortable to interact more closely with it and for extended periods of time.

This paper focuses on curating interactive art in experimental ways. It takes the experiment outside labs and into institutions and cityscapes in the form of prototype exhibitions to be evaluated through the medium of audience engagement. The PhD research focuses on authenticating the audience's experience of interactive art; first defining parameters for authenticity within fine arts and creative robotics, then examining how, through the application of evaluative frameworks to iterative exhibition processes, one might capture and utilize experience as a medium in itself. The exhibitions for examination are/will be produced by PhD researcher Deborah Turnbull Tillman through her research initiative New Media Curation (NMC).

This paper investigates the possibility of recognising individual persons from their walking gait using three-dimensional 'skeleton' data from an inexpensive consumer-level sensor, the Microsoft 'Kinect'. In an experimental pilot study it is shown that the K-means algorithm - as a candidate unsupervised clustering algorithm - is able to cluster gait samples from four persons with a nett accuracy of 43.6%.

Gesture recognition is an important aspect of interpersonal social interaction. Developing a similar capacity in a robot will improve human-robot interaction. Various unsupervised clustering methods applied to clustering a set of dynamic human arm gestures are compared. Unsupervised clustering is important in gesture recognition as it imposes no a priori bound on the set of gestures. Results are compared using v-measure, a metric that allows differential weighting between clustering homogeneity and completeness. Experiments show that the best clustering method depends on the desired balance between homogeneity and completeness.

During social interaction, humans extract important information from tactile stimuli that improves their understanding of the interaction. The development of a similar capacity in a robot will contribute to the future success of intuitive human-robot interaction. This paper presents a method of touch sensing based on the principle of electrical impedance tomography (EIT) that can be used to implement a large, flexible and stretchable artificial sensitive skin for robots. A classifier based on the “LogitBoost” algorithm is used to classify the modality of six different types of touch on an experimental EIT-based skin. Experiments showed that the modality of touch was correctly classified in approximately 80% of the trials. This is comparable with the experimental accuracy of a human touch recipient. The classification accuracies show significant improvements from previous classification algorithms applied to different artificial sensitive skins.

Electrical impedance tomography (EIT) is a technique used to estimate the internal conductivity of an electrically conductive body by using measurements made only at its boundary. If this body is made of a thin, flexible, and stretchable material that responds to touch with local changes in conductivity, it can be used to create an artificial sensitive skin. Mathematically, the EIT reconstruction problem is an ill-posed nonlinear inverse problem in which it is commonly assumed that electrodes are located only on the surface of the body. In a thin sensitive skin, however, electrodes can readily be located within the 2-D conducting domain. This paper compares existing electrode-drive patterns with new patterns in which a number of reference electrodes are located inside of the sensitive skin. Simulation results and experimental data show improvements in both resolution and robustness to noise of the reconstructed image. These improvements are shown to be consistent for several commonly used regularization methods.