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In essence, successful grasp boils down to correct responses to multiple contact events between fingertips and objects. In most scenarios, tactile sensing is adequate to distinguish contact events. Due to the nature of high dimensionality…

机器人学 · 计算机科学 2019-10-10 Yazhan Zhang , Weihao Yuan , Zicheng Kan , Michael Yu Wang

Tactile sensing is essential for robots to perceive and react to the environment. However, it remains a challenge to make large-scale and flexible tactile skins on robots. Industrial machine knitting provides solutions to manufacture…

机器人学 · 计算机科学 2023-03-07 Zilin Si , Tianhong Catherine Yu , Katrene Morozov , James McCann , Wenzhen Yuan

Tactile sensing is an essential capability for robots that carry out dexterous manipulation tasks. While cameras, Lidars and other remote sensors can assess a scene globally and instantly, tactile sensors can reduce their measurement…

机器人学 · 计算机科学 2021-12-06 Daniel Fernandes Gomes , Shan Luo

Optical tactile sensors are extensively utilized in intelligent robot manipulation due to their ability to acquire high-resolution tactile information at a lower cost. However, achieving adequate reality and versatility in simulating…

机器人学 · 计算机科学 2024-05-07 Zirong Shen , Yuhao Sun , Shixin Zhang , Zixi Chen , Heyi Sun , Fuchun Sun , Bin Fang

Adding tactile sensors to a robotic system is becoming a common practice to achieve more complex manipulation skills than those robotics systems that only use external cameras to manipulate objects. The key of tactile sensors is that they…

机器人学 · 计算机科学 2023-05-09 Julio Castaño-Amoros , Pablo Gil

Sensors and Artificial Intelligence (AI) have revolutionized the analysis of human movement, but the scarcity of specific samples presents a significant challenge in training intelligent systems, particularly in the context of diagnosing…

Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility…

机器人学 · 计算机科学 2024-10-21 Wadhah Zai El Amri , Malte Kuhlmann , Nicolás Navarro-Guerrero

Vision-based learning from demonstrations has achieved remarkable success in enabling robots to perform manipulation tasks and high-level semantic reasoning, yet it remains insufficient for complex, contact-rich manipulation. While there is…

Tactile sensing is essential to the human perception system, so as to robot. In this paper, we develop a novel optical-based tactile sensor "FingerVision" with effective signal processing algorithms. This sensor is composed of soft skin…

机器人学 · 计算机科学 2018-10-08 Yazhan Zhang , Zicheng Kan , Yu Alexander Tse , Yang Yang , Michael Yu Wang

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…

机器人学 · 计算机科学 2026-02-16 Iris Andrussow , Jans Solano , Benjamin A. Richardson , Georg Martius , Katherine J. Kuchenbecker

Accurate real-time tracking of dexterous hand movements and interactions has numerous applications in human-computer interaction, metaverse, robotics, and tele-health. Capturing realistic hand movements is challenging because of the large…

As more robots are implemented for contact-rich tasks, tactile sensors are in increasing demand. For many circumstances, the contact is required to be compliant, and soft sensors are in need. This paper introduces a novelly designed soft…

机器人学 · 计算机科学 2021-05-18 Wu-Te Yang , Zhian Kuang , Changhao Wang , Masayoshi Tomizuka

Tactile sensing provides robots with rich feedback during manipulation, enabling a host of perception and controls capabilities. Here, we present a new open-source, vision-based tactile sensor designed to promote reproducibility and…

机器人学 · 计算机科学 2024-10-01 Andrea Sipos , William van den Bogert , Nima Fazeli

Humans are able to convey different messages using only touch. Equipping robots with the ability to understand social touch adds another modality in which humans and robots can communicate. In this paper, we present a social gesture…

机器人学 · 计算机科学 2025-03-24 Dakarai Crowder , Kojo Vandyck , Xiping Sun , James McCann , Wenzhen Yuan

Regulating grasping force to reduce slippage during dynamic object interaction remains a fundamental challenge in robotic manipulation, especially when objects are manipulated by multiple rolling contacts, have unknown properties (such as…

机器人学 · 计算机科学 2025-12-25 Cheng-Yu Kuo , Hirofumi Shin , Takamitsu Matsubara

Tactile sensing has been used for a variety of robotic exploration and manipulation tasks but a common constraint is a requirement for a large amount of training data. This paper addresses the issue of data-efficiency by proposing a novel…

机器人学 · 计算机科学 2020-08-13 Elizabeth A. Stone , Nathan F. Lepora , David A. W. Barton

Tactile perception is crucial for a variety of robot tasks including grasping and in-hand manipulation. New advances in flexible, event-driven, electronic skins may soon endow robots with touch perception capabilities similar to humans.…

信号处理 · 电气工程与系统科学 2020-08-19 Fuqiang Gu , Weicong Sng , Tasbolat Taunyazov , Harold Soh

Distributed tactile sensing remains difficult to scale over large areas: dense sensor arrays increase wiring, cost, and fragility, while many alternatives provide limited coverage or miss fast interaction dynamics. We present Sound of…

机器人学 · 计算机科学 2026-02-20 Xili Yi , Ying Xing , Zachary Manchester , Nima Fazeli

One of the most important object properties that humans and robots perceive through touch is hardness. This paper investigates information-theoretic active sampling strategies for sample-efficient hardness classification with vision-based…

Tactile and kinesthetic perceptions are crucial for human dexterous manipulation, enabling reliable grasping of objects via proprioceptive sensorimotor integration. For robotic hands, even though acquiring such tactile and kinesthetic…

机器人学 · 计算机科学 2025-09-11 Ce Guo , Xieyuanli Chen , Zhiwen Zeng , Zirui Guo , Yihong Li , Haoran Xiao , Dewen Hu , Huimin Lu