Redirected walking (RDW) allows people to explore large virtual environments while walking within a smaller physical space. When physical space is limited, explicit resets such as turn-in-place are unavoidable. Previous studies have reduced the frequency of resets by adjusting user paths. However, resets remain necessary under severe spatial constraints. The frequent use of turn-in-place resets interrupts locomotion and can degrade task performance and user experience. We present Non-Euclidean Portal Reset (NEPR), a reset technique that enables continuous experiences without pausing the user's walking. When a collision risk is detected, NEPR opens a virtual portal leading to a short, non-Euclidean corridor. Traversing the corridor repositions and reorients the user. The exit returns the user near the point of interest target in the primary world, maintaining flow. To evaluate the effectiveness of NEPR, we conducted user experiments comparing (1) the conventional turn-in-place reset, (2) NEPR, and (3) a hybrid method combining both approaches. Our results demonstrate that NEPR and the combined technique significantly improve the user experience and task performance compared to the traditional method. Overall, NEPR reframes resets as seamless transitions rather than interruptions, enhancing the practicality of RDW.

Eye gaze is regarded as a promising interaction modality in extended reality (XR) environments. However, to address the challenges posed by the Midas touch problem, the determination of selection intention frequently relies on the implementation of additional manual selection techniques, such as explicit gestures (e.g., controller/hand inputs or dwell), which are inherently limited in their functionality. We hereby present a machine learning (ML) model based on the Bayesian framework, which is employed to predict user selection intention in real-time, with the unique distinction that all data used for training and prediction are obtained from gaze data alone. The model utilizes a Bayesian approach to transform gaze data into selection probabilities, which are subsequently fed into an ML model to discern selection intentions. In Study 1, a high-performance model was constructed, enabling real-time inference using solely gaze data. This approach was found to enhance performance, thereby validating the efficacy of the proposed methodology. In Study 2, a user study was conducted to validate a manual-free technique based on the prediction model. The advantages of eliminating explicit gestures and potential applications were also discussed.

The reset technique of Redirected Walking (RDW) forcibly reorients the user's direction overtly to avoid collisions with boundaries, obstacles, or other users in the physical space. However, excessive resetting can decrease the user's sense of immersion and presence. Several RDW studies have been conducted to address this issue. Among them, much research has been done on reset techniques that reduce the number of resets by devising reset direction rules or optimizing them for a given environment. However, existing optimization studies on reset techniques have mainly focused on a single-user environment. In a multi-user environment, the dynamic movement of other users and static obstacles in the physical space increase the possibility of resetting. In this study, we propose Multi-Agent Reinforcement Resetter (MARR), which resets the user taking into account both physical obstacles and multi-user movement to minimize the number of resets. MARR is trained using multi-agent reinforcement learning to determine the optimal reset direction in different environments. This approach allows MARR to effectively account for different environmental contexts, including arbitrary physical obstacles and the dynamic movements of other users in the same physical space. We compared MARR to other reset technologies through simulation tests and user studies, and found that MARR outperformed the existing methods. MARR improved performance by learning the optimal reset direction for each subtle technique used in training. MARR has the potential to be applied to new subtle techniques proposed in the future. Overall, our study confirmed that MARR is an effective reset technique in multi-user environments.

Resetting in redirected walking (RDW) allows users to maintain a continuous, collision-free walking experience in virtual reality (VR), even in a limited physical space. Since frequent resets reduce the user's sense of immersion, extensive research has been conducted to develop resetters that provide optimal reset directions. Various visual reset user interfaces (UIs) have been proposed to help users perform the correct reset direction according to the improved resetter, but their effectiveness has not been sufficiently verified. In addition, expert interviews conducted to identify the problems in the current reset process revealed that users sometimes fail to recognize the visual reset UI in time. Therefore, we propose a novel visual reset UI using Gauge, which is expected to provide users with an effective and high-quality experience. In Study 1, we demonstrate the effectiveness of the Gauge UI by comparing it to existing UIs (Direction, End Point, and Arrow Alignment). Users of various locomotion techniques, including RDW, inevitably need to perform resets, and in this work we propose a novel paradigm: a combined multimodal reset interface.

In this paper, we present the Selective Redirection Controller (SRC), which selects the optimal redirection controller based on the physical and virtual environment in Redirected Walking (RDW). The primary advantage of SRC over existing controllers is its dynamic switching among four different redirection controllers (S2C, TAPF, ARC, and SRL) based on the user's environment, as opposed to using a single fixed controller throughout the experience. By switching between redirection controllers based on the context around the user, SRC aims to optimize the advantages of each redirection strategy. The SRC model is trained using reinforcement learning to dynamically and instantaneously switch redirection controllers based on the user's environment. We evaluated the performance of SRC against traditional redirection controllers through simulations and user studies conducted in various physical and virtual environments. The findings indicate that SRC reduces the number of resets significantly compared to traditional redirection controllers. Heat map visualization was utilized during the development process to analyze which redirection controller SRC chooses based on the different environments around the user. SRC alternates between redirection techniques based on the user's environment, maximizing the advantages of each strategy for a superior RDW experience.