Having the ability to estimate an object's properties through interaction will enable robots to manipulate novel objects. Object's dynamics, specifically the friction and inertial parameters have only been estimated in a lab environment with precise and often external sensing. Could we infer an object's dynamics in the wild with only the robot's sensors? In this paper, we explore the estimation of dynamics of a grasped object in motion, with tactile force sensing at multiple fingertips. Our estimation approach does not rely on torque sensing to estimate the dynamics. To estimate friction, we develop a control scheme to actively interact with the object until slip is detected. To robustly perform the inertial estimation, we setup a factor graph that fuses all our sensor measurements on physically consistent manifolds and perform inference. We show that tactile fingertips enable in-hand dynamics estimation of low mass objects.
Compliant In-hand Rolling Manipulation Using Tactile Sensing
Center for Robotics and Biosystems and the Department of Mechanical Engineering, Northwestern University（机器人与生物系统中心和机械工程系，西北大学）；Northwestern Institute on Complex Systems (NICO)（复杂系统研究所（NICO））
TacPrint: A Wearable Fingertip Tactile Sensor for Human-to-Robot Contact Reproduction
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Imprintx Robotics（Imprintx机器人公司）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
Adding internal audio sensing to internal vision enables human-like in-hand fabric recognition with soft robotic fingertips
Haptic Intelligence Department, Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所人机交互部门）；Department for Distributed Intelligence / Autonomous Learning, University of Tübingen（图宾根大学分布式智能/自主学习部门）
Self-Supervised Anchoring of Fingertip Sensing to Proprioception and Proactive Actions for Robot Imitation Learning
National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所（AIST））；CNRS-AIST JRL (Joint Robotics Laboratory), IRL（法国国家科学研究中心-产业技术综合研究所联合机器人实验室（JRL），IRL）
Remote Surfaces at Your Fingertips: Electrovibration-Based Tactile Feedback for Robot Teleoperation via Touchscreen Interfaces
University of the West of England（西英格兰大学）；Bristol Robotics Laboratory（布里斯托机器人实验室）；ENSTA, Institut Polytechnique de Paris（巴黎综合理工学院ENSTA）；Autonomous Systems and Robotics Lab（自主系统与机器人实验室）
FingerEye: Learning Dexterous Manipulation with Continuous Vision-Tactile Sensing
National University of Singapore（新加坡国立大学）；RoboScience（机器人科学）；Huazhong University of Science and Technology（华中科技大学）；South China University of Technology（华南理工大学）
A Haptic Robot Finger Designed for Guqin Instrument Playing
The Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；The Chinese University of Hong Kong - Shenzhen（香港中文大学（深圳））；School of Physics and Optoelectronic Engineering, Shenzhen University（深圳大学物理与光电工程学院）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Tactile SoftHand-A: 3D-Printed, Tactile, Highly-underactuated, Anthropomorphic Robot Hand with an Antagonistic Tendon Mechanism
School of Engineering Mathematics and Technology（工程数学与技术学院）；Bristol Robotics Laboratory（布里斯托尔机器人实验室）；University of Bristol（布里斯托尔大学）；School of Robotics（机器人学院）；Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学）；Department of Information Engineering（信息工程系）；Research Center "E.Piaggio"（E.Piaggio研究中心）；University of Pisa（比萨大学）；Soft Robotics for Human Cooperation and Rehabilitation（人机合作与康复软机器人）；Istituto Italiano di Tecnologia（意大利技术研究院）
Tactile Model O: Fabrication and testing of a 3d-printed, three-fingered tactile robot hand
University of Bristol（布里斯托大学）；Bristol Robotics Laboratory（布里斯托机器人实验室）
为机器人手带来触觉将允许更有效的抓握，以及类似人类触摸的广泛好处。在这里，我们提出了一个3D打印的三指触觉机器人手，包括一个定制的OpenHand Model O，用于在每个手指的远端指骨中容纳TacTip柔软的仿生触觉传感器。我们期望将这种欠驱动手的抓取能力与复杂的触觉感知相结合，将导致一个有效的机器人手研究平台-触觉模型O（T-MO）。该设计使用了三个JeVois机器视觉系统，每个系统都包括一个位于触觉指尖的微型摄像头和一个位于手底部的处理模块。为了评估T-MO的能力，我们使用YCB对象集上的抓取器评估基准对其抓取性能进行基准测试。触觉感知的能力进行评估，通过执行触觉对象分类的26个对象，并预测是否把握将成功地解除每个对象。结果与最新技术水平一致，利用了应用于触觉图像输出的深度学习的进步。总的来说，这项工作表明，T-MO是一个有效的平台，机器人手的研究，我们希望它打开了一系列的应用在自主物体处理。补充视频：https://youtu.be/RTcCpgffCrQ。
3D Vision-tactile Reconstruction from Infrared and Visible Images for Robotic Fine-grained Tactile Perception
State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology（数字制造装备与技术国家重点实验室，机械科学与工程学院，华中科技大学）
AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning
Shanghai Jiao Tong University（上海交通大学）；Ant Group（蚂蚁集团）；Shanghai Innovation Institute（上海创新研究院）；Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）
