Trust is essential for social interactions, including those between humans and social artificial agents, such as robots. Several factors and combinations thereof can contribute to the formation of trust and, importantly in the case of machines that work with a certain margin of error, to its maintenance and repair after it has been breached. In this article, we present the results of a study aimed at investigating the role of robot voice and chosen repair strategy on trust formation and repair in a collaborative task. People helped a robot navigate through a maze, and the robot made mistakes at pre-defined points during the navigation. Via in-game behaviour and follow-up questionnaires, we could measure people's trust towards the robot. We found that people trusted the robot speaking with a state-of-the-art synthetic voice more than with the default robot voice in the game, even though they indicated the opposite in the questionnaires. Additionally, we found that three repair strategies that people use in human-human interaction (justification of the mistake, promise to be better and denial of the mistake) work also in human-robot interaction.

Multisensory integration influences emotional perception, as the McGurk effect demonstrates for the communication between humans. Human physiology implicitly links the production of visual features with other modes like the audio channel: Face muscles responsible for a smiling face also stretch the vocal cords that result in a characteristic smiling voice. For artificial agents capable of multimodal expression, this linkage is modeled explicitly. In our studies, we observe the influence of visual and audio channels on the perception of the agents' emotional expression. We created videos of virtual characters and social robots either with matching or mismatching emotional expressions in the audio and visual channels. In two online studies, we measured the agents' perceived valence and arousal. Our results consistently lend support to the ‘emotional McGurk effect' hypothesis, according to which face transmits valence information, and voice transmits arousal. When dealing with dynamic virtual characters, visual information is enough to convey both valence and arousal, and thus audio expressivity need not be congruent. When dealing with robots with fixed facial expressions, however, both visual and audio information need to be present to convey the intended expression.

There is a reciprocal relationship between trust and vocal communication in human interactions. On one hand, a predisposition towards trust is necessary for communication to be meaningful and effective. On the other hand, we use vocal cues to signal our own trustworthiness and to infer it from the speech of others. Research on trustworthiness attributions to vocal characteristics is scarce and contradictory, however, being typically based on explicit judgements which may not predict actual trust-oriented behaviour. We use a game theory paradigm to examine the influence of speaker accent and prosody on trusting behaviour towards a simulated game partner, who responds either trustworthily or untrustworthily in an investment game. We found that speaking in a non-regional standard accent increases trust, as does relatively slow articulation rate. The effect of accent persists over time, despite the accumulation of clear evidence regarding the speaker’s level of trustworthiness in a negotiated interaction. Accents perceived as positive for trust can maintain this benefit even in the face of behavioural evidence of untrustworthiness.

Recent work identified a concerning trend of disproportional gender representation in research participants in Human-Computer Interaction (HCI). Motivated by the fact that Human-Robot Interaction (HRI) shares many participant practices with HCI, we explored whether this trend is mirrored in our field. By producing a dataset covering participant gender representation in all 684 full papers published at the HRI conference from 2006-2021, we identify current trends in HRI research participation. We find an over-representation of men in research participants to date, as well as inconsistent and/or incomplete gender reporting, which typically engages in a binary treatment of gender at odds with published best practice guidelines. We further examine if and how participant gender has been considered in user studies to date, in-line with current discourse surrounding the importance and/or potential risks of gender based analyses. Finally, we complement this with a survey of HRI researchers to examine correlations between who is doing with the who is taking part, to further reflect on factors which seemingly influence gender bias in research participation across different sub-fields of HRI. Through our analysis, we identify areas for improvement, but also reason for optimism, and derive some practical suggestions for HRI researchers going forward.

When deploying robots, its physical characteristics, role, and tasks are often fixed. Such factors can also be associated with gender stereotypes among humans, which then transfer to the robots. One factor that can induce gendering but is comparatively easy to change is the robot's voice. Designing voice in a way that interferes with fixed factors might therefore be a way to reduce gender stereotypes in human-robot interaction contexts. To this end, we have conducted a video-based online study to investigate how factors that might inspire gendering of a robot interact. In particular, we investigated how giving the robot a gender-ambiguous voice can affect perception of the robot. We compared assessments (n=111) of videos in which a robot's body presentation and occupation mis/matched with human gender stereotypes. We found evidence that a gender-ambiguous voice can reduce gendering of a robot endowed with stereotypically feminine or masculine attributes. The results can inform more just robot design while opening new questions regarding the phenomenon of robot gendering.

Sound can benefit human-robot interaction, but little work has explored questions on the design of nonverbal sound for robots. The unique confluence of sound design and robotics expertise complicates these questions, as most roboticists do not have sound design expertise, necessitating collaborations with sound designers. We sought to understand how roboticists and sound designers approach the problem of robot sound design through two qualitative studies. The first study followed discussions by robotics researchers in focus groups, where these experts described motivations to add robot sound for various purposes. The second study guided music technology students through a generative activity for robot sound design; these sound designers in-training demonstrated high variability in design intent, processes, and inspiration. To unify the two perspectives, we structured recommendations through the design thinking framework, a popular design process. The insights provided in this work may aid roboticists in implementing helpful sounds in their robots, encourage sound designers to enter into collaborations on robot sound, and give key tips and warnings to both.

This paper proposes a novel method to develop gender-ambiguous TTS, which can be used to investigate hidden gender bias in speech perception. Our aim is to provide a tool for researchers to conduct experiments on language use associated with specific genders. Ambiguous voices can also be beneficial for virtual assistants, to help reduce stereotypes and increase acceptance. Our approach uses a multi-speaker embedding in a neural TTS engine, combining two corpora recorded by a male and a female speaker to achieve a gender-ambiguous timbre. We also propose speaker-disentangled prosody control to ensure that the timbre is robust across a range of prosodies and enable more expressive speech. We optimised the output using an SSL-based network trained on hundreds of speakers. We conducted perceptual evaluations on the settings that were judged most ambiguous by the network, which showed that listeners perceived the speech samples as gender-ambiguous, also in prosody-controlled conditions.

Trust is essential for social interactions, including those between humans and social artificial agents, such as robots. Several robot-related factors can contribute to the formation of trust. However, previous work has often treated trust as an absolute concept, whereas it is highly context-dependent, and it is possible that some robot-related features will influence trust in some contexts, but not in others. In this paper, we present the results of two video-based online studies aimed at investigating the role of robot voice and empathic behaviour on trust formation in a general context as well as in a task-specific context. We found that voice influences trust in the specific context, with no effect of voice or empathic behaviour in the general context. Thus, context mediated whether robot-related features play a role in people's trust formation towards robots.

Signal Temporal Logic (STL) is a rigorous specification language that allows one to express various spatiotemporal requirements and preferences. Its semantics (called robustness) allows quantifying to what extent are the STL specifications met. In this work, we focus on enabling STL constraints and preferences in the Real-Time Rapidly ExploringRandom Tree (RT-RRT*) motion planning algorithm in an environment with dynamic obstacles. We propose a cost function that guides the algorithm towards the asymptotically most robust solution, i.e. a plan that maximally adheres to the STL specification. In experiments, we applied our method to a social navigation case, where the STL specification captures spatio-temporal preferences on how a mobile robot should avoid an incoming human in a shared space. Our results show that our approach leads to plans adhering to the STL specification, while ensuring efficient cost computation.

Signal Temporal Logic (STL) is a rigorous specification language that allows one to express various spatio-temporal requirements and preferences. Its semantics (called robustness) allows quantifying to what extent are the STL specifications met. In this work, we focus on enabling STL constraints and preferences in the Real-Time Rapidly Exploring Random Tree (RT-RRT*) motion planning algorithm in an environment with dynamic obstacles. We propose a cost function that guides the algorithm towards the asymptotically most robust solution, i.e. a plan that maximally adheres to the STL specification. In experiments, we applied our method to a social navigation case, where the STL specification captures spatio-temporal preferences on how a mobile robot should avoid an incoming human in a shared space. Our results show that our approach leads to plans adhering to the STL specification, while ensuring efficient cost computation.