With recent advances in information and communication technologies, Hybrid meetings, where local attendees are physically present and remote participants join virtually, have become increasingly common. However, remote participants often experience reduced contextual awareness and a sense of isolation. To address these issues, we propose HybridSphere, a hybrid meeting system that reconstructs a shared virtual reality environment for remote participants. The system employs a 360-degree camera and pose estimation to generate real-time avatar representations of local attendees to allow remote users with head-mounted displays to experience the meeting as if all participants are in the same virtual space. We conducted a user study comparing HybridSphere to a baseline condition in which remote participants viewed an unmodified 360-degree video. Although the avatars were rated lower in perceived trustworthiness and likability due to limited visual fidelity, participants appreciated the seated-6DoF function. These results suggest that immersive and spatially flexible VR representations can enhance remote engagement in hybrid meetings.

Autonomous robot manipulation is a complex and continuously evolving robotics field. This paper focuses on data augmentation methods in imitation learning. Imitation learning consists of three stages: data collection from experts, learning model, and execution. However, collecting expert data requires manual effort and is time-consuming. Additionally, as sensors have different data acquisition intervals, preprocessing such as downsampling to match the lowest frequency is necessary. Downsampling enables data augmentation and also contributes to the stabilization of robot operations. In light of this background, this paper proposes the Data Augmentation Method for Bilateral Control-Based Imitation Learning with Images, called “DABI”. DABI collects robot joint angles, velocities, and torques at 1000 Hz, and uses images from gripper and environmental cameras captured at 100 Hz as the basis for data augmentation. This enables a tenfold increase in data. In this paper, we collected just 5 expert demonstration datasets. We trained the bilateral control Bi-ACT model with the unaltered dataset and two augmentation methods for comparative experiments and conducted real-world experiments. The results confirmed a significant improvement in success rates, thereby proving the effectiveness of DABI. For additional material, please check: https://mertcookimg.github.io/dabi

We present Bi-LAT, a novel imitation learning framework that unifies bilateral control with natural language processing to achieve precise force modulation in robotic manipulation. Bi-LAT leverages joint position, velocity, and torque data from leader-follower teleoperation while also integrating visual and linguistic cues to dynamically adjust applied force. By encoding human instructions such as "softly grasp the cup" or "strongly twist the sponge" through a multimodal Transformer-based model, Bi-LAT learns to distinguish nuanced force requirements in real-world tasks. We demonstrate Bi-LAT’s performance in (1) unimanual cup-stacking scenario where the robot accurately modulates grasp force based on language commands, and (2) bimanual sponge-twisting task that requires coordinated force control. Experimental results show that Bi-LAT effectively reproduces the instructed force levels, particularly when incorporating SigLIP among tested language encoders. Our findings demonstrate the potential of integrating natural language cues into imitation learning, paving the way for more intuitive and adaptive human–robot interaction. For additional material, please visit the website: https://mertcookimg.github.io/bi-lat/

Mobile robot navigation systems are increasingly relied upon in dynamic and complex environments, yet they often struggle with map inaccuracies and the resulting inefficient path planning. This paper presents MRHaD, a Mixed Reality-based Hand-drawn Map Editing Interface that enables intuitive, real-time map modifications through natural hand gestures. By integrating the MR head-mounted display with the robotic navigation system, operators can directly create hand-drawn restricted zones (HRZ), thereby bridging the gap between 2D map representations and the real-world environment. Comparative experiments against conventional 2D editing methods demonstrate that MRHaD significantly improves editing efficiency, map accuracy, and overall usability, contributing to safer and more efficient mobile robot operations. The proposed approach provides a robust technical foundation for advancing human-robot collaboration and establishing innovative interaction models that enhance the hybrid future of robotics and human society. For additional material, please check: https://mertcookimg.github.io/mrhad/

Virtual reality (VR) video games that are played on a VR headset are becoming increasingly common in households, and though many games require players to navigate vast virtual spaces, most homes cannot provide a large enough physical space to encompass the entire virtual space. Thus, VR video games that require locomotion often provide users with alternative locomotion techniques. While teleportation or steering is typically used as a standard, new techniques can overcome remaining problems, such as motion sickness. However, a holistic perspective of user needs and issues regarding these techniques in practical situations has not been studied on a broad basis. To address this gap in the literature and contribute to future VR video game development and research, we conducted 16 semi-structured interviews and surveyed 88 participants to help explore issues regarding existing locomotion techniques. Our results revealed preferences related to teleportation versus steering and the postures that users adopt while playing VR video games, along with user needs for locomotion techniques in each posture.

Head tracking allows users of Virtual Reality (VR) to freely rotate their heads 360 degrees while exploring virtual environments. When using VR in a limited space, the ability to physically rotate one's head is limited to a specific range. To address this issue, previous studies have proposed methods to employ distinct rotation factors for real and imaginary rotations. However, its primary usage lies in redirected walking; thus, it is unsuitable for seated VR. In this article, we propose CaliView, which consistently adjusts the user's perspective to always face an optimal direction in VR, all while ensuring a comfortable posture. CaliView continuously controls the rotation gain to ensure that the disparity between the present body orientation and the optimal orientation is reduced to zero, encouraging the user to assume a forward-facing position with the target orientation. To assess the suggested approach, CaliView, we experimented to compare three conditions: CaliView, snap turning only (SnapTurn), and hybrid of CaliView and snap turning (Hybrid). The research findings suggest that CaliView functions as a useful reorientation technique, enabling implicit reorientation without sacrificing the user experience. Additionally, this study showcases its compatibility with various other techniques, such as the traditional snap-turn, thus emphasizing its versatility.

Due to the inherent uncertainty in their deformability during motion, previous methods in deformable object manipulation, such as rope and cloth, often required hundreds of real-world demonstrations to train a manipulation policy for each object, which hinders their applications in our ever-changing world. To address this issue, we introduce GenDOM, a framework that allows the manipulation policy to handle different deformable objects with only a single real-world demonstration. To achieve this, we augment the policy by conditioning it on deformable object parameters and training it with a diverse range of simulated deformable objects so that the policy can adjust actions based on different object parameters. At the time of inference, given a new object, GenDOM can estimate the deformable object parameters with only a single real-world demonstration by minimizing the disparity between the grid density of point clouds of real-world demonstrations and simulations in a differentiable physics simulator. Empirical validations on both simulated and real-world object manipulation setups clearly show that our method can manipulate different objects with a single demonstration and significantly outperforms the baseline in both environments (a 62% improvement for in-domain ropes and a 15% improvement for out-of-distribution ropes in simulation, as well as a 26% improvement for ropes and a 50% improvement for cloths in the real world), demonstrating the effectiveness of our approach in one-shot deformable object manipulation. https://sites.google.com/view/gendom/home.

This paper proposes a tracking control method considering an obstacle avoidance by a reflective motion for a mobile robot. In the mobile robot, a laser range finder is utilized to recognize environmental information. The obstacle avoid motion is created by using environmental information and the virtual manipulators mounted on the mobile robot. In the conventional researches, the flexible obstacle avoidance method by using the kinematic relation between the virtual manipulators and environmental information was reported. This method has the advantage for the calculation cost and flexibility like reflective motion. However, the robot may not follow the desired trajectory after the obstacle avoidance, since this conventional method is based on the velocity control. In order to solve this problem, this paper proposes a combination method of the tracking control and the obstacle avoidance. Considering the velocity limits of the tracking control, it is possible to modify the priority of the trajectory tracking and the obstacle avoidance. The effectiveness of the proposed method was confirmed from the experimental results.