相关论文: GelSight EndoFlex: A Soft Endoskeleton Hand with C…
The tactile sensing capabilities of human hands are essential in performing daily activities. Simultaneously perceiving normal and shear forces via the mechanoreceptors integrated into the hands enables humans to achieve daily tasks like…
The purpose of this study is to construct a contact point estimation system for the both side of a finger, and to realize a motion of bending the finger after inserting the finger into a tool (hereinafter referred to as the bending after…
We present an advance in wearable technology: a mobile-optimized, real-time, ultra-low-power event camera system that enables natural hand gesture control for smart glasses, dramatically improving user experience. While hand gesture…
Tactile sensing is critical in advanced interactive systems by emulating the human sense of touch to detect stimuli. Vision-based tactile sensors are promising for providing multimodal capabilities and high robustness, yet existing…
Dynamic hand tracking and gesture recognition is a hard task since there are many joints on the fingers and each joint owns many degrees of freedom. Besides, object occlusion is also a thorny issue in finger tracking and posture…
Deep learning combined with high-resolution tactile sensing could lead to highly capable dexterous robots. However, progress is slow because of the specialist equipment and expertise. The DIGIT tactile sensor offers low-cost entry to…
Distinguishing the feel of smooth silk from coarse cotton is a trivial everyday task for humans. When exploring such fabrics, fingertip skin senses both spatio-temporal force patterns and texture-induced vibrations that are integrated to…
Existing grasp controllers usually either only support finger-tip grasps or need explicit configuration of the inner forces. We propose a novel grasp controller that supports arbitrary grasp types, including power grasps with…
Autonomous grasping of novel objects that are previously unseen to a robot is an ongoing challenge in robotic manipulation. In the last decades, many approaches have been presented to address this problem for specific robot hands. The…
Robotic dexterous grasping is a challenging problem due to the high degree of freedom (DoF) and complex contacts of multi-fingered robotic hands. Existing deep reinforcement learning (DRL) based methods leverage human demonstrations to…
Our research investigates vibrotactile perception in four prosthetic hands with distinct kinematics and mechanical characteristics. We found that rigid and simple socket-based prosthetic devices can transmit tactile information and…
Robotic manipulation has made significant advancements, with systems demonstrating high precision and repeatability. However, this remarkable precision often fails to translate into efficient manipulation of thin deformable objects. Current…
Handling fragile objects remains a major challenge for robotic manipulation. Tactile sensing and soft robotics can improve delicate object handling, but typically involve high integration complexity or slow response times. We address these…
In this paper, we introduce RealDex, a pioneering dataset capturing authentic dexterous hand grasping motions infused with human behavioral patterns, enriched by multi-view and multimodal visual data. Utilizing a teleoperation system, we…
Soft sensors have continued growing interest in robotics, due to their ability to enable both passive conformal contact from the material properties and active contact data from the sensor properties. However, the same properties of…
Increasing the performance of tactile sensing in robots enables versatile, in-hand manipulation. Vision-based tactile sensors have been widely used as rich tactile feedback has been shown to be correlated with increased performance in…
A new fluid-driven soft robot hand in this study uses the idea of the bionics and has the anthropomorphic form, which is oriented to the flexible grasp function. The soft robot hand is composed of a new kind of multi-freedom soft finger and…
We propose a fully automatic method for learning gestures on big touch devices in a potentially multi-user context. The goal is to learn general models capable of adapting to different gestures, user styles and hardware variations (e.g.…
Grasping is a core task in robotics with various applications. However, most current implementations are primarily designed for rigid items, and their performance drops considerably when handling fragile or deformable materials that require…
Soft and lightweight grippers have greatly enhanced the performance of robotic manipulators in handling complex objects with varying shape, texture, and stiffness. However, the combination of universal grasping with passive sensing…