Previous research has established redirected walking as a potential answer to exploring large virtual environments via natural locomotion within a limited physical space. However, much of the previous work has either focused on investigating human perception of redirected walking illusions or developing novel redirection techniques. In this paper, we take a broader look at the problem and formalize the concept of a complete redirected walking system. This work establishes the theoretical foundations for combining multiple redirection strategies into a unified framework known as adaptive redirection. This meta-strategy adapts based on the context, switching between a suite of strategies with a priori knowledge of their performance under the various circumstances. This paper also introduces a novel static planning strategy that optimizes gain parameters for a predetermined virtual path, known as the Combinatorially Optimized Pre-Planned Exploration Redirector (COPPER). We conducted a simulation-based experiment that demonstrates how adaptation rules can be determined empirically using machine learning, which involves partitioning the spectrum of contexts into regions according to the redirection strategy that performs best. Adaptive redirection provides a foundation for making redirected walking work in practice and can be extended to improve performance in the future as new techniques are integrated into the framework.

Developing effective strategies for redirected walking requires extensive evaluations across a variety of factors that influence performance. Because these large-scale experiments are often not practical with user studies, researchers have instead utilized simulations to systematically test different algorithm parameters, physical space configurations, and virtual walking paths. Although simulation offers an efficient way to evaluate redirected walking algorithms, it remains an open question whether this evaluation methodology is ecologically valid. In this paper, we investigate the interaction between locomotion behavior and redirection gains at a micro-level (across small path segments) and macro-level (across an entire experience). This examination involves analyzing data from real users and comparing algorithm performance metrics with a simulated user model. The results identify specific properties of user locomotion behavior that influence the application of redirected walking gains and resets. Overall, we found that the simulation provided a conservative estimate of the average performance with real users and observed that performance trends when comparing two redirected walking algorithms were preserved. In general, these results indicate that simulation is an empirically valid evaluation methodology for redirected walking algorithms.

The 2019 VGTC Virtual Reality Best Dissertation Award goes to Qi Sun, a 2018 graduate from Stony Brook University, for his dissertation entitled, “Computational Methods for Immersive Perception.”

This paper discusses the design and technical choices of a proof of concept migrating video game character that can move from a game environment to a holographic environment rendered on a novel holographic light field display. We pair these two environments with interactions that are consistent to each, using a game controller for interaction in the game environment and voice, gesture and face tracking in the holographic environment. Finally, we carried out a pilot study to assess the level of social presence, consistent migration and coherent experience in our proposed system.

In recent years there has been an explosive proliferation of virtual assistants such as Amazon's Alexa or Google's Home Assistant. These virtual assistants are becoming increasingly sophisticated and able to manage many aspects of our lives. Through bundled sales campaigns, consumers are encouraged to install multiple devices throughout the house and to share their use across the household. Unfortunately, competing devices do not behave consistently with each other. Furthermore, the increased popularity of third party behavior extensions (e.g. Alexa Skills or Google Home Actions) has the potential to add to this behavioral discontinuity. Ambiguities in multi-user behavior combined with disparate agent and extension behavior can lead to consumer confusion when the agent does not behave as expected. This report documents ongoing research intended to identify users' inherent assumptions about a smart agent's behavior through user studies. The results can be applied to inform design guidelines that lead to a more consistent user experience, with agents clarifying their behavior in situations identified as ambiguous.

As the focus of virtual reality technology is shifting from singleperson experiences to multi-user interactions, it becomes increasingly important to accommodate multiple co-located users within a shared real-world space. For locomotion and navigation, the introduction of multiple users moving both virtually and physically creates additional challenges related to potential user-on-user collisions. In this work, we focus on defining the extent of these challenges, in order to apply redirected walking to two users immersed in virtual reality experiences within a shared physical tracked space. Using a computer simulation framework, we explore the costs and benefits of splitting available physical space between users versus attempting to algorithmically prevent user-to-user collisions. We also explore fundamental components of collision prevention such as steering the users away from each other, forced stopping, and user re-orientation. Each component was analyzed for the number of potential disruptions to the flow of the virtual experience. We also develop a novel collision prevention algorithm that reduces overall interruptions by 17.6% and collision prevention events by 58.3%. Our results show that sharing space using our collision prevention method is superior to subdividing the tracked space.

Redirected walking enables the exploration of large virtual environments while requiring only a finite amount of physical space. Unfortunately, in living room sized tracked areas the effectiveness of common redirection algorithms such as Steer-to-Center is very limited. A potential solution is to increase redirection effectiveness by applying two types of perceptual manipulations (curvature and translation gains) simultaneously. This paper investigates how such combination may affect detection thresholds for curvature gain. To this end we analyze the estimation methodology and discuss selection process for a suitable estimation method. We then compare curvature detection thresholds obtained under different levels of translation gain using two different estimation methods: method of constant stimuli and Green's maximum likelihood procedure. The data from both experiments shows no evidence that curvature gain detection thresholds were affected by the presence of translation gain (with test levels spanning previously estimated interval of undetectable translation gain levels). This suggests that in practice currently used levels of translation and curvature gains can be safely applied simultaneously. Furthermore, we present some evidence that curvature detection thresholds may be lower that previously reported. Our estimates indicate that users can be redirected on a circular arc with radius of either 11.6m or 6.4m depending on the estimation method vs. the previously reported value of 22m. These results highlight that the detection threshold estimates vary significantly with the estimation method and suggest the need for further studies to define efficient and reliable estimation methodology.

Manipulating a virtual object with appropriate passive haptic cues provides a satisfying sense of presence in virtual reality. However, scaling such experiences to support multiple virtual objects is a challenge as each one needs to be accompanied with a precisely-located haptic proxy object. We propose a solution that overcomes this limitation by hacking human perception. We have created a framework for repurposing passive haptics, called haptic retargeting, that leverages the dominance of vision when our senses conflict. With haptic retargeting, a single physical prop can provide passive haptics for multiple virtual objects. We introduce three approaches for dynamically aligning physical and virtual objects: world manipulation, body manipulation and a hybrid technique which combines both world and body manipulation. Our study results indicate that all our haptic retargeting techniques improve the sense of presence when compared to typical wand-based 3D control of virtual objects. Furthermore, our hybrid haptic retargeting achieved the highest satisfaction and presence scores while limiting the visible side-effects during interaction.

Manipulating a virtual object with appropriate passive haptic cues provides a satisfying sense of presence in virtual reality. However, scaling such experiences to support multiple virtual objects is a challenge as each one needs to be accompanied with a precisely-located haptic proxy object. We showcase a solution that overcomes this limitation by hacking human perception. Our framework for repurposing passive haptics, called haptic retargeting, leverages the dominance of vision when our senses conflict. With haptic retargeting, a single physical prop can provide passive haptics for multiple virtual objects. We introduce three approaches for dynamically aligning physical and virtual objects: body manipulation, world manipulation and a hybrid technique which combines both world and body manipulation. This demo has been presented previously at CHI 2016.

Redirected walking techniques have been introduced to overcome physical limitations for natural locomotion in virtual reality. Although subtle perceptual manipulations are helpful to keep users within relatively small tracked spaces, it is inevitable that users will approach critical boundary limits. Current solutions to this problem involve breaks in presence by introducing distractors, or freezing the virtual world relative to the user's perspective. We propose an approach that integrates into the virtual world narrative to draw users' attention and to cause them to temporarily alter their course to avoid going off bounds. This method ties together unnoticeable translation, rotation, and curvature gains, efficiently reorienting the user while maintaining the user's sense of immersion. We also discuss how this new method can be effectively used in conjunction with other reorientation techniques.

Redirected Walking is technique that leverages human perception characteristics to allow locomotion in virtual environments larger than the tracking area. Among the many redirection techniques, some strictly depend on the user's current position and orientation, while more recent algorithms also depend on the user's predicted behavior. This prediction serves as an input to a computationally expensive search to determine an optimal path. The search output is formulated as a series of gains to be applied at different stages along the path. An example prediction could be if a user is walking down a corridor, a natural prediction would be that the user will walk along a straight line down the corridor, and she will choose one of the possible directions with equal probability. In practice, deviations from the expected virtual path are inevitable, and as a result, the real world path traversed will differ from the original prediction. These deviations can not only force the search to select a less optimal path in the next iteration, but also in cases cause the users to go off bounds, requiring resets, causing a jarring experience for the user. We propose a method to account for these deviations by modifying the redirection gains per update frame, aiming to keep the user on the intended predicted physical path.

Redirected walking techniques enable natural locomotion through immersive virtual environments that are considerably larger than the available real world walking space. However, the most effective strategy for steering the user remains an open question, as most previously presented algorithms simply redirect toward the center of the physical space. In this work, we present a theoretical framework that plans a walking path through a virtual environment and calculates the parameters for combining translation, rotation, and curvature gains such that the user can traverse a series of defined waypoints efficiently based on a utility function. This function minimizes the number of overt reorientations to avoid introducing potential breaks in presence. A notable advantage of this approach is that it leverages knowledge of the layout of both the physical and virtual environments to enhance the steering strategy.