Astrictive-type grippers, which generate gripping forces from adhesive forces at the contact surface, such as suction cup, are popular end-effectors as picking solutions because of their simplicity and small working space. However, the adhesive force of the astrictive gripper decreases with increasing complexity of the object surface; thus, its application has been restricted to simple picking of objects with a flat surface. Here, in this article, we present an all-round honeycomb astrictive gripper that has an orthotropic surface tension for grasping highly irregular shaped objects with an uneven surface. The design is inspired by mimicking the two-level (macro- and mesoscale) shape adaptation of the octopus's leg. The stiffness-variable structure is also consisted to change its stiffness similar to the function of octopus's leg, and owing to the combination of these structures makes possible to perform various tasks, including hammering, breakfast serving, and vaccination, which were not possible for previous astrictive gripper.

In this paper, we present an intelligent system that is capable of estimating the status of a player engaging in winter activities based on the sequence analysis of multivariate time-series sensor signals. Among the winter activities, this paper mainly focuses on downhill winter sports such as alpine skiing and snowboarding. Assuming that the mechanical vibrations generated by physical interaction between the ground surface and ski/snowboard in motion can describe the ground conditions and playing contexts, we utilize inertial and vibration signals to categorize the motion context. For example, the proposed system estimates whether the player is sitting on a ski lift or standing on the escalator, or skiing on wet or snowy ground, etc. To measure the movement of a player during a game or on the move, we develop a custom embedded system comprising a motion sensor and piezo transducer. The captured multivariate sequence signals are then trained in a supervised fashion. We adopt artificial neural network approaches (e.g., 1D convolutional neural network, and gated recurrent neural networks, such as long short-term memory and gated recurrent units). The experimental results validate the feasibility of the proposed approach.

Handheld virtual reality (VR) controllers are necessary for creating immersive experiences. In this paper, we propose a gated RNN-based sequence model that estimates the joint torques of a serially linked handheld VR system interface from a sequential position input. In our previous study, we proposed a motion planning algorithm for articulated systems based on the active contour model that optimizes the positions of each joint torque based on the measured base position (6-Degrees of Freedom). Because the position-toposition scheme, which calculates the joint positions from a given base position, illustrated several limitations concerning safety (i.e. unable to handle unexpected contact with the surroundings), our current study proposes a position-to-torque generation scheme that estimates the joint torques from the measured base position sequences. To that end, we trained the sequences of joint torques and the sequence of the 6-DoF base position as a supervised learning task. To model the multivariate temporal information of the sequences, we employed a gated recurrent unit. The experimental results validate the successful generation of joint trajectory profiles.

This paper proposes a novel tangible interface system to enhance the immersive experience in virtual reality environments. The proposed system allows representing physical properties of the tool, such as the deflection of the elastic rods, which users handle in a virtual and physical environment. This system is composed of two parts; one is an articulated interface to visually represent the physical behavior and the other is a computational algorithm that can compute a set of 6-DOF positions of the links. The proposed computational algorithm extends an active contour model, which is used primarily in computer vision and image processing, incorporating a spring and damping constraint energy functional. An elastic rod is modeled as a series of rigid line segments with a symmetric relationship between neighboring segments, and its shape is modeled to be influenced by energies that are induced by a user, and the external deformation of the spline. The symmetric and sparse properties of the proposed model enable an efficient energy minimization process, and the modification of a number of the line segments. Based on this configuration, we construct an energy generation method based on the positional displacement of the base element to generate the deflection behavior of the contour according to the user’s motion in the space. Therefore, the physical device can simulate a variety of deformable objects by modulating energy parameters during the energy minimization process. Experimental results demonstrate the feasibility of emulating various behaviors of deformable splines, and applying to virtual reality system without interfering with the motion-to-photon latency. We also discuss the method’s limitations and explore its potential.

Despite the rapid expansion of EVs (Electric Vehicles), current EV charging systems remain inconvenient for users. To address this issue, robot-assisted EV charging systems are emerging as promising solutions, requiring precise EV inlet localization as a key technology. However, existing vision-based methods face fundamental trade-offs: 2D RGB provides dense visual information but suffers from illumination sensitivity and depth ambiguity, while 3D PC (Point Cloud) provides geometric precision with limitations in sparse structures and high computational complexity. To overcome these challenges, we propose MDSNet (Multimodal Dual Stream Network), a novel framework that combines 2D and 3D CNNs through two key innovations: (1) modality-specific axial attention mechanisms for efficient feature fusion, and (2) hierarchical data slicing, tailored to each modality-specific characteristic. Unlike single-modality approaches, MDSNet effectively leverages complementary strengths while mitigating individual weaknesses of both RGB and PC data. The proposed method is evaluated on RGB-PC data pairs acquired from realistic EV scenarios under various real-world environments including multiple distances, viewing angles, and illumination variations. Experimental results demonstrate state-of-the-art performance, establishing MDSNet as a practical solution for autonomous robot-assisted EV charging systems.