Virtual environments (VEs) are increasingly used for immersive experiences, training simulations, and entertainment, yet factors such as height perception and user stance can significantly influence user experience (UX). Height perception in VEs plays a crucial role in shaping UX, particularly in immersive applications such as climbing simulations. This study investigates the effects of height in various VEs and examines how user stance, sitting or standing, impacts immersion, perceived height, and motion sickness. A user study was conducted with 25 participants who played through five randomized climbing scenarios, ranging from indoor climbing gyms to outdoor cityscapes and mountainous terrains. Participants’ UX was assessed using standardized questionnaires, including the IPQ for general presence, spatial presence, involvement, and experienced realism; as well as the SSQ to evaluate motion sickness symptoms such as nausea, oculomotor strain, and disorientation. Results indicate that seated participants experienced slightly higher immersion but were also more susceptible to motion sickness compared to those standing. While standing participants maintained consistent scores across different environments, seated participants reported increased immersion and discomfort as the VEs became larger, more physically demanding, and visually complex.

A range of situations exist in which it would be useful to influence people's behavior in public spaces, for example to improve the efficiency of passenger flow in congested train stations. We have identified our previously developed Robot Centric paradigm of Human-Robot Interaction (HRI), which positions robots as Interaction Peers, as a potentially suitable model to achieve more effective influence through defining and exploiting the interactivity of robots (that is, their ability to moderate their issued sociocontextual cues based on the behavioral information read from humans). In this paper, we investigate whether increasing a robot's interactivity will increase the effectiveness of its influence on people in public spaces. A two-part study (total n = 273) was conducted in both a major Australian public train station (n = 84 + 105) and a university (n = 84) where passersby encountered a robot, designed with various levels of interactivity, which attempted to influence their passage. The findings suggest that the Robot Centric HRI paradigm generalizes to other robots and application spaces, and enables deliberate moderation of a robot's interactivity, facilitating more nuanced, predictable and systematic influence, and thus yielding greater effectiveness.

Visual Focus of Attention is an important mechanism to support successful interactions. In order to communicate effectively and intentionally (issuing cues when a person is paying attention, for example), a robot must have an understanding of this Visual Focus of Attention behavior in the Human-Robot Interaction space. A real-world interaction study was conducted with 24 unsolicited participants to explore attention behavior towards robots in this space. The results suggest there is no generalizable attention pattern between people, and thus that online, in situ Visual Focus of Attention estimation would be advantageous to Human-Robot Interaction.

Navigating and path planning in environments with limited a priori knowledge is a fundamental challenge for mobile robots. Robots operating in human-occupied environments must also respect sociocontextual boundaries such as personal workspaces. There is a need for robots to be able to navigate in such environments without having to explore and build an intricate representation of the world. In this paper, a method for supplementing directly observed environmental information with indirect observations of occupied space is presented. The proposed approach enables the online inclusion of novel human positional traces and environment information into a probabilistic framework for path planning. Encapsulation of sociocontextual information, such as identifying areas that people tend to use to move through the environment, is inherently achieved without supervised learning or labelling. Our method bootstraps navigation with indirectly observed sensor data, and leverages the flexibility of the Gaussian process (GP) for producing a navigational map that sampling based path planers such as Probabilistic Roadmaps (PRM) can effectively utilise. Empirical results on a mobile platform demonstrate that a robot can efficiently and socially-appropriately reach a desired goal by exploiting the navigational map in our Bayesian statistical framework.

The industrial revolution undoubtedly defined the role of machines in our society, and it directly shaped the paradigm for human machine interaction - a paradigm which was inherited by the field of Human Robot Interaction (HRI) as the machines became robots. This paper argues that, for a foreseeable set of interactions, reshaping this paradigm would result in more effective and more often successful interactions. This paper presents our Robot Centric paradigm for HRI. Evidence in the form of summaries of relevant literature and our past efforts in developing social-robotics enabling technology is presented to support our paradigm. A definition and a set of recommendations for designing the key enabling component, sociocontextual cues, of our paradigm are presented. Finally, empirical evidence generated through a number of experiments and field studies (N = 456 and N = 320) demonstrates our paradigm is both feasibly incorporated into HRI and moreover, yields significant contributions to the successfulness of a set of HRIs.

Ensuring that an interaction is initiated with a particular and unsuspecting member of a group is a complex task. As a first step the robot must effectively, expediently and reliably recognise the humans as they carry on with their typical behaviours (in situ). A method for constructing a scale and viewing angle robust feature vector (from analysing a 3D pointcloud) designed to encapsulate the inter-person variations in the size and shape of the people's head to shoulder region (Head-to-shoulder signature - HSS) is presented. Furthermore, a method for utilising said feature vector as the basis of person recognition via a Support-Vector Machine is detailed. An empirical study was performed in which person recognition was attempted on in situ data collected from 25 participants over 5 days in a office environment. The results report a mean accuracy over the 5 days of 78.15% and a peak accuracy 100% for 9 participants. Further, the results show a considerably better-than-random (1/23 = 4.5%) result for when the participants were: in motion and unaware they were being scanned (52.11%), in motion and face directly away from the sensor (36.04%), and post variations in their general appearance. Finally, the results show the HSS has considerable ability to accommodate for a person's head, shoulder and body rotation relative to the sensor - even in cases where the person is faced directly away from the robot.

Ensuring that a particular and unsuspecting member of a group is the recipient of a salient-item hand-over is a complicated interaction. The robot must effectively, expediently and reliably communicate its intentions to advert any tendency within the group towards antinormative behaviour. In this paper, we study how a robot can establish the participant roles of such an interaction using imitated social and contextual cues. We designed two gaze cues, the first was designed to discourage antinormative behaviour through individualising a particular member of the group and the other to the contrary. We designed and conducted a field experiment (456 participants in 64 trials) in which small groups of people (between 3 and 20 people) assembled in front of the robot, which then attempted to pass a salient object to a particular group member by presenting a physical cue, followed by one of two variations of a gaze cue. Our results showed that presenting the individualising cue had a significant (z=3.733, p=0.0002) effect on the robot's ability to ensure that an arbitrary group member did not take the salient object and that the selected participant did.

As the desire to see robots ubiquitous in society grows, so does the need for providing the robots with the means of building awareness of any humans with which it may be sharing the environment. This paper presents a real-world suitable system which enables robots to robustly perceive the presence of people acoustically. The proposed binaural system first identifies voiced signal by means of a novel approach to Voice Activity Detection that exploits the spectral signature and characteristics of speech without reliance on a priori knowledge. Bearing estimates for each speaker are then made using a multi-track particle filter with a belief update function comprised of a Cross-correlation bearing estimate and an estimate of the speaker's fundamental frequency. Results, from an evaluation of each of the major system components and a system evaluation in which the robot successfully built human-centric situational awareness of the three humans with which it shared an office lunch-room containing typical background noises, are presented and discussed.