Takuji Narumi

Yong-Hao Hu, Sotaro Yokoi, Yuji Hatada et al.

Online experiments using metaverse platforms have gained significant traction in Human-Computer Interaction and Virtual Reality (VR) research. However, current research workflows are highly fragmented, as researchers must use separate tools for system implementation, participant recruitment, experiment execution, and data collection, reducing consistency and increasing workload. We present LUIDA (Large-scale Unified Infrastructure for Digital Assessments), a metaverse-based framework that integrates these fragmented processes. LUIDA automatically allocates interconnected virtual environments for parallel experiment execution and provides implementation templates adaptable to various VR research domains, requiring minimal metaverse development expertise. Our evaluation included two studies using a prototype built on Cluster, the commercial metaverse platform. First, VR researchers using LUIDA to develop and run experiments reported high usability scores (SUS: 73.75) and moderate workload (NASA-TLX: 24.11) for overall usage, with interviews confirming streamlined workflows compared to traditional laboratory experiments. Second, we conducted three replicated experiments with public Cluster users, each recruiting approximately 200 participants within one week. These experiments produced results that closely matched the original studies, validating the experimental integrity of LUIDA across research domains. After technical refinements, we plan to release LUIDA as an open platform, providing a standardized protocol to improve research efficiency and experimental reproducibility in VR studies.

Shaoyu Cai, Kening Zhu, Yuki Ban et al.

Existing psychophysical studies have revealed that the cross-modal visual-tactile perception is common for humans performing daily activities. However, it is still challenging to build the algorithmic mapping from one modality space to another, namely the cross-modal visual-tactile data translation/generation, which could be potentially important for robotic operation. In this paper, we propose a deep-learning-based approach for cross-modal visual-tactile data generation by leveraging the framework of the generative adversarial networks (GANs). Our approach takes the visual image of a material surface as the visual data, and the accelerometer signal induced by the pen-sliding movement on the surface as the tactile data. We adopt the conditional-GAN (cGAN) structure together with the residue-fusion (RF) module, and train the model with the additional feature-matching (FM) and perceptual losses to achieve the cross-modal data generation. The experimental results show that the inclusion of the RF module, and the FM and the perceptual losses significantly improves cross-modal data generation performance in terms of the classification accuracy upon the generated data and the visual similarity between the ground-truth and the generated data.

Rebecca Fribourg, Nami Ogawa, Ludovic Hoyet et al.

In this paper, we introduce a concept called ''virtual co-embodiment'', which enables a user to share their virtual avatar with another entity (e.g., another user, robot, or autonomous agent). We describe a proof-of-concept in which two users can be immersed from a first-person perspective in a virtual environment and can have complementary levels of control (total, partial, or none) over a shared avatar. In addition, we conducted an experiment to investigate the influence of users' level of control over the shared avatar and prior knowledge of their actions on the users' sense of agency and motor actions. The results showed that participants are good at estimating their real level of control but significantly overestimate their sense of agency when they can anticipate the motion of the avatar. Moreover, participants performed similar body motions regardless of their real control over the avatar. The results also revealed that the internal dimension of the locus of control, which is a personality trait, is negatively correlated with the user's perceived level of control. The combined results unfold a new range of applications in the fields of virtual-reality-based training and collaborative teleoperation, where users would be able to share their virtual body.

Yuchen Chang, Keigo Matsumoto, Takuji Narumi et al.

There is a growing demand for redirected walking (RDW) techniques and their application. To apply appropriate RDW methods and manipulation, the RDW controllers are predominantly used. There are three types of RDW controllers: direct scripted controller, generalized controller, and predictive controller. The scripted controller type pre-scripts the mapping between the real and virtual environments. The generalized controller type employs the RDW method and manipulation quantities according to a certain procedure depending on the user's position in relation to the real space. This approach has the potential to be reused in any environment; however, it is not fully optimized. The predictive controller type predicts the user's future path using the user's behavior and manages RDW techniques. This approach is highly anticipated to be very effective and versatile; however, it has not been sufficiently developed. This paper proposes a novel RDW controller using reinforcement learning (RL) with advanced plannability/versatility. Our simulation experiments indicate that the proposed method can reduce the number of reset manipulations, which is one of the indicators of the effectiveness of the RDW controller, compared to the generalized controller under real environments with many obstacles. Meanwhile, the experimental results also showed that the gain output by the proposed method oscillates. The results of a user study conducted showed that the proposed RDW controller can reduce the number of resets compared to the conventional generalized controller. Furthermore, no adverse effects such as cybersickness associated with the oscillation of the output gain were evinced. The simulation and user studies demonstrate that the proposed RDW controller with RL outperforms the existing generalized controllers and can be applied to users.