17 accepted papers
Wearable Inertial Measurement Units (IMUs) allow non-intrusive motion tracking, but limited sensor placements can introduce uncertainty in capturing detailed full-body movements. Existing methods mitigate this issue by selecting more physically plausible motion patterns but do not directly address i…
Knowing the gripping force being applied to an object is important for improving the quality of the grip, as well as preventing surface damage or breakage of fragile objects. In the case of soft grippers, however, an attaching or embedding of force/pressure sensors can compromise their adaptability
This study addresses the challenge of performing visual localization in demanding conditions such as night-time scenarios adverse weather and seasonal changes. While many prior studies have focused on improving image matching performance to facilitate reliable dense keypoint matching between images…
Despite the prevalence of robotic manipulation tasks in various real-world applications of different requirements and needs, there has been a lack of focus on enhancing the adaptability of robotic grasping systems. Most of the current literature constructs models around a single gripper, succumbing
This study addresses an image-matching problem in challenging cases, such as large scene variations or textureless scenes. To gain robustness to such situations, most previous studies have attempted to encode the global contexts of a scene via graph neural networks or transformers. However, these co…
Knowledge graphs represent human knowledge using triplets composed of entities and relations. While most existing knowledge graph embedding methods only consider the structure of a knowledge graph, a few recently proposed multimodal methods utilize images or text descriptions of entities in a knowle…
Multi-view stereo (MVS) is a crucial task for precise 3D reconstruction. Most recent studies tried to improve the performance of matching cost volume in MVS by introducing a skilled design to cost formulation or cost regularization. In this paper, we focus on learning robust feature extraction to en…
Semantic grasping aims to make stable robotic grasps suitable for specific object manipulation tasks. While existing semantic grasping models focus only on the grasping regions of objects based on their affordances, reasoning about which gripper to use for grasping, e.g., a rigid parallel-jaw grippe
Learning from human feedback using event-related electroencephalography (EEG) signals has attracted extensive attention recently owing to their intuitive communication ability by decoding user intentions. However, this approach requires users to perform specified tasks and their success or failure.…
Soft wearable robotic gloves based on tendon-sheath mechanism are widely developed for assisting people with a loss of hand mobility. For these robots, knowing the fingertip forces applied to deformable objects is crucial in successfully grasping them without causing excessive deformations. Existing
Accurately detecting multiple simultaneous touches is crucial for various applications using piezoresistance sensor arrays. However, calibrating them is difficult due to their nonlinearity and hysteresis. While data-driven deep learning approaches could model complex sensor patterns, the required am
Multi-view stereo (MVS) algorithms have been commonly used to model large-scale structures. When processing MVS, image acquisition is an important issue because its reconstruction quality depends heavily on the acquired images. Recently, an explore-then-exploit strategy has been used to acquire imag…
Soft wearable hand robots with tendon-sheath mechanisms are being actively developed to assist people with lost hand mobility. For these robots, accurately estimating fingertip forces leads to successful object grasping. An approach can utilize information from actuators assuming quasi-static enviro
Nowadays, the use of deep learning for the calibration of soft wearable sensors has addressed the typical drawbacks of the microfluidic soft sensors, such as hysteresis and nonlinearity. However, previous studies have not yet resolved some of the design constraints such as the sensors are needed to
In this study, we addressed a path planning problem of a mobile robot to construct highly accurate 3D models of an unknown environment. Most studies have focused on exploration approaches, which find the most informative viewpoint or trajectories by analyzing a volumetric map. However, the completio…
Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems, such as soft mobile robots, soft wearable robots, and soft grippers. However, major drawbacks of soft sensors compared with traditional sensors are their nonlinearity and hysteresis
In this paper, we propose a novel algorithm for planning exploration paths to generate 3D models of unknown environments by using a micro-aerial vehicle (MAV). Our algorithm initially determines a next-best-view (NBV) that maximizes information gain and plans a collision-free path to reach the NBV.…