Vision-based Tactile Sensors (VBTSs) show significant promise in that they can leverage image measurements to provide high-spatial-resolution human-like performance. However, current VBTS designs, typically confined to the fingertips of robotic grippers, prove somewhat inadequate, as many grasping and manipulation tasks require multiple contact points with the object. With an end goal of enabling large-scale, multi-surface tactile sensing via VBTSs, our research (i) develops a synchronized image acquisition system with minimal latency,(ii) proposes a modularized VBTS design for easy integration into finger phalanges, and (iii) devises a zero-shot calibration approach to improve data efficiency in the simultaneous calibration of multiple VBTSs. In validating the system within a miniature 3-fingered robotic gripper equipped with 7 VBTSs we demonstrate improved tactile perception performance by covering the contact surfaces of both gripper fingers and palm. Additionally, we show that our VBTS design can be seamlessly integrated into various end-effector morphologies significantly reducing the data requirements for calibration.
University of Bristol（布里斯托大学）；Bristol Robotics Laboratory（布里斯托机器人实验室）
Bringing tactile sensation to robotic hands will allow for more effective grasping, along with the wide range of benefits of human-like touch. Here we present a 3D-printed, three-fingered tactile robot hand comprising an OpenHand Model O customized to house a TacTip soft biomimetic tactile sensor in the distal phalanx of each finger. We expect that combining the grasping capabilities of this underactuated hand with sophisticated tactile sensing will result in an effective platform for robot hand research -- the Tactile Model O (T-MO). The design uses three JeVois machine vision systems, each c…
Hong Kong University of Science and Technology（香港科技大学）；Nanjing University of Information Science and Technology（南京信息工程大学）
Monitoring the state of contact is essential for robotic devices, especially grippers that implement gecko-inspired adhesives where intimate contact is crucial for a firm attachment. However, due to the lack of deformable sensors, few have demonstrated tactile sensing for gecko grippers. We present Viko, an adaptive gecko gripper that utilizes vision-based tactile sensors to monitor contact state. The sensor provides high-resolution real-time measurements of contact area and shear force. Moreover, the sensor is adaptive, low-cost, and compact. We integrated gecko-inspired adhesives into the se…
To adapt to constantly changing environments and be safe for human interaction, robots should have compliant and soft characteristics as well as the ability to sense the world around them. Even so, the incorporation of tactile sensing into a soft compliant robot, like the Fin Ray finger, is difficult due to its deformable structure. Not only does the frame need to be modified to allow room for a vision sensor, which enables intricate tactile sensing, the robot must also retain its original mechanically compliant properties. However, adding high-resolution tactile sensors to soft fingers is dif…
This work details the design of a novel two finger robot gripper with multiple Gelsight based optical-tactile sensors covering the inner surface of the hand. The multiple Gelsight sensors can gather the surface topology of the object from multiple views simultaneously as well as can track the shear and tensile stress. In addition, other sensing modalities enable the hand to gather the thermal, acoustic and vibration information from the object being grasped. The force controlled gripper is fully actuated so that it can be used for various grasp configurations and can also be used for in-hand m…
DePaul University（德保罗大学）；University of Arkansas（阿肯色大学）；Clemson University（克莱姆森大学）
We propose a novel tri-fingered soft robotic gripper with decoupled stiffness and shape control capability for performing adaptive grasping with minimum system complexity. The proposed soft fingers adaptively conform to object shapes facilitating the handling of objects of different types, shapes, and sizes. Each soft gripper finger has an inextensible articulable backbone and is actuated by pneumatic muscles. We derive a kinematic model of the gripper and use an empirical approach to map input pressures to stiffness and bending deformation of fingers. We use these mappings to achieve decouple…
Nagoya University（名古屋大学）；Technical University of Darmstadt（达姆施塔特工业大学）
Vision-based tactile sensors have gained extensive attention in the robotics community. The sensors are highly expected to be capable of extracting contact information i.e. haptic information during in-hand manipulation. This nature of tactile sensors makes them a perfect match for haptic feedback applications. In this paper, we propose a contact force estimation method using the vision-based tactile sensor DIGIT, and apply it to a position-force teleoperation architecture for force feedback. The force estimation is done by building a depth map for DIGIT gel surface deformation measurement and…
The synthesis of tactile sensing with compliance is essential to many fields, from agricultural usages like fruit picking, to sustainability practices such as sorting recycling, to the creation of safe home-care robots for the elderly to age with dignity. From tactile sensing, we can discern material properties, recognize textures, and determine softness, while with compliance, we are able to securely and safely interact with the objects and the environment around us. These two abilities can culminate into a useful soft robotic gripper, such as the original GelSight Fin Ray, which is able to g…
Hybrid Soft Electrostatic Metamaterial Gripper for Multi-surface, Multi-object Adaptation
Empa - Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科学与技术实验室（Empa））；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Imperial College London（帝国理工学院）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（英联邦科学与工业研究组织（CSIRO））
Soft grippers are gaining momentum across applications due to their flexibility and dexterity. However, the infinite-dimensionality and non-linearity associated with soft robots challenge modeling and closed-loop control of soft grippers to perform grasping tasks. To solve this problem, data-driven methods have been proposed. Most data-driven methods rely on intensive model learning in simulation or offline, and as such it may be hard to generalize across different settings not explicitly trained upon and in physical robot testing where online control is required. In this paper, we propose an …
This letter proposes a novel single-fingered reconfigurable robotic gripper for grasping objects in narrow working spaces. The finger of the developed gripper realizes two configurations, namely, the insertion and grasping modes, using only a single motor. In the insertion mode, the finger assumes a thin shape such that it can insert its tip into a narrow space. The grasping mode of the finger is activated through a folding mechanism. Mode switching can be achieved in two ways: switching the mode actively by a motor, or combining passive rotation of the fingertip through contact with the suppo…
National University of Singapore（新加坡国立大学）；Guilin University of Electronic Technology（桂林电子科技大学）
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 grasping delicate objects. In this paper, we design and fabricate a novel biomimetic tactile sensor with skin-like heterogeneity that perceives normal and shear contact forces simultaneously. It mimics the multilayers of mechanoreceptors by combining an extrinsic layer (piezoresistive sensors) and an intrinsic layer (a Hall sensor) so that it can perform estim…
Magnetic Tactile-Driven Soft Actuator for Intelligent Grasping and Firmness Evaluation
Soft BioRobotics Perception Lab, Istituto Italiano di Tecnologia (IIT)（软生物机器人感知实验室，意大利技术研究院）；The BioRobotics Institute, Scuola Superiore Sant’Anna (SSSA)（生物机器人研究所，圣安娜高等学院）；Department of Haptic Intelligence, CVTE Guangzhou Shiyuan Electronics Co., Ltd（触觉智能部门，广州世渊电子有限公司）；Università degli Studi di Genova (UniGe)（热那亚大学）
Soft optical tactile sensors enable robots to manipulate deformable objects by capturing important features such as high-resolution contact geometry and estimations of object compliance. This work presents a variable stiffness soft tactile end-effector called StRETcH, a Soft to Resistive Elastic Tactile Hand, that is easily manufactured and integrated with a robotic arm. An elastic membrane is suspended between two robotic fingers, and a depth sensor capturing the deformations of the elastic membrane enables sub-millimeter accurate estimates of contact geometries. The parallel-jaw gripper vari…
Multi-fingered hands could be used to achieve many dexterous manipulation tasks, similarly to humans, and tactile sensing could enhance the manipulation stability for a variety of objects. However, tactile sensors on multi-fingered hands have a variety of sizes and shapes. Convolutional neural networks (CNN) can be useful for processing tactile information, but the information from multi-fingered hands needs an arbitrary pre-processing, as CNNs require a rectangularly shaped input, which may lead to unstable results. Therefore, how to process such complex shaped tactile information and utilize…
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）
In this paper we explore state-of-the-art underactuated, compliant robot gripper designs through looking at their performance on a generic grasping task. Starting from a state of the art open gripper design, we propose design modifications,and importantly, evaluate all designs on a grasping experiment involving a selection of objects resulting in 3600 object-gripper interactions. Interested in non-planned grasping but rather on a design's generic performance, we explore the influence of object shape, pose and orientation relative to the gripper and its finger number and configuration. Using op…
Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp. Most of the works in the literature represented tactile readings as plain feature vectors or matrix-like tactile images, using them to train machine learning models. In this work, we explore an alternative way of exploiting tactile information to predict grasp stability by leveraging graph-like representations of tactile data, which preserve the actual spatial arrangement of the sensor's taxels and their locality. In experimentation, we …
University of Alicante（阿利坎特大学）；Computer Science Research Institute, University of Alicante（阿利坎特大学计算机科学研究所）
Tactile sensors supply useful information during the interaction with an object that can be used for assessing the stability of a grasp. Most of the previous works on this topic processed tactile readings as signals by calculating hand-picked features. Some of them have processed these readings as images calculating characteristics on matrix-like sensors. In this work, we explore how non-matrix sensors (sensors with taxels not arranged exactly in a matrix) can be processed as tactile images as well. In addition, we prove that they can be used for predicting grasp stability by training a Convol…
A GelSight sensor uses an elastomeric slab covered with a reflective membrane to measure tactile signals. It measures the 3D geometry and contact force information with high spacial resolution, and successfully helped many challenging robot tasks. A previous sensor, based on a semi-specular membrane, produces high resolution but with limited geometry accuracy. In this paper, we describe a new design of GelSight for robot gripper, using a Lambertian membrane and new illumination system, which gives greatly improved geometric accuracy while retaining the compact size. We demonstrate its use in m…
Single-grasp deformable object discrimination: the effect of gripper morphology, sensing modalities, and action parameters
Czech Technical University in Prague（布拉格捷克理工大学）；Faculty of Electrical Engineering, Czech Technical University in Prague（布拉格捷克理工大学电气工程学院）；Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague（布拉格捷克理工大学捷克信息学、机器人学与控制论研究所）
M3D-skin: Multi-material 3D-printed Tactile Sensor with Hierarchical Infill Structures for Pressure Sensing
Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo（机械信息学系，信息科学和技术研究生院，东京大学）；AI Center, Graduate School of Information Science and Technology, The University of Tokyo（人工智能中心，信息科学和技术研究生院，东京大学）
BMW Group（宝马集团）；University of Glasgow（格拉斯哥大学）；Technische Universität München（慕尼黑工业大学）；Donders Institute for Brain and Cognition（唐德斯脑与认知研究所）；Radboud University（拉德堡德大学）
This paper proposes a novel active visuo-tactile based methodology wherein the accurate estimation of the time-invariant SE(3) pose of objects is considered for autonomous robotic manipulators. The robot equipped with tactile sensors on the gripper is guided by a vision estimate to actively explore and localize the objects in the unknown workspace. The robot is capable of reasoning over multiple potential actions, and execute the action to maximize information gain to update the current belief of the object. We formulate the pose estimation process as a linear translation invariant quaternion …
Everything-Grasping (EG) Gripper: A Universal Gripper with Synergistic Suction-Grasping Capabilities for Cross-Scale and Cross-State Manipulation
Department of Mechanical Engineering, University of California, Berkeley（加州大学伯克利分校机械工程系）；Department of Biomedical Engineering, The Chinese University of Hong Kong（香港中文大学生物医学工程系）；Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong（香港中文大学机械与自动化工程系）；Multi-scale Medical Robotics Center, Hong Kong, China（香港医学机器人多尺度中心）；Department of Agricultural and Biological Engineering, Purdue University（普渡大学农业与生物工程系）
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）
Robotics and Biology Laboratory, Technische Universität Berlin（技术大学柏林机器人与生物学实验室）；Science of Intelligence, Research Cluster of Excellence, Berlin（柏林智能科学卓越研究中心）；Robotics Institute Germany（德国机器人研究所）
Imperial College London（帝国理工学院）；Ocado Technology（奥凯多科技）
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 UniGrasp framework, introduced recently, has the ability to generalize to different types of robotic grippers; however, this method does not work on grippers with closed-loop constraints and is data-inefficient when applied to robot hands with multigrasp configurations. In this paper, we present EfficientGrasp, a generalized grasp synthesis and gripper control me…
University of Bristol（布里斯托大学）；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）
Tactile sensing is used by humans when grasping to prevent us dropping objects. One key facet of tactile sensing is slip detection, which allows a gripper to know when a grasp is failing and take action to prevent an object being dropped. This study demonstrates the slip detection capabilities of the recently developed Tactile Model O (T-MO) by using support vector machines to detect slip and test multiple slip scenarios including responding to the onset of slip in real time with eleven different objects in various grasps. We demonstrate the benefits of slip detection in grasping by testing tw…
Curvature-Aware Calibration of Tactile Sensors for Accurate Force Estimation on Non-Planar Surfaces
School of Electrical and Computer Engineering（电气与计算机工程学院）；Cornell University（康奈尔大学）；Sibley School of Mechanical and Aerospace Engineering（机械与航空航天工程学院）；Dept. of Mechanical Engineering（机械工程系）；Virginia Tech（弗吉尼亚理工大学）
NVIDIA（英伟达）；Georgia Institute of Technology（佐治亚理工学院）；University of Utah（犹他大学）
Using simulation to train robot manipulation policies holds the promise of an almost unlimited amount of training data, generated safely out of harm's way. One of the key challenges of using simulation, to date, has been to bridge the reality gap, so that policies trained in simulation can be deployed in the real world. We explore the reality gap in the context of learning a contextual policy for multi-fingered robotic grasping. We propose a Grasping Objects Approach for Tactile (GOAT) robotic hands, learning to overcome the reality gap problem. In our approach we use human hand motion demonst…
In-Hand Object Pose Estimation via Visual-Tactile Fusion
Department of Computer Science, Goethe Universität Frankfurt, Germany（弗赖堡大学计算机科学系）；Intelligent Autonomous Systems Lab, Department of Computer Science, TU Darmstadt, Germany（图恩-达姆斯塔德大学计算机科学系智能自主系统实验室）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Centre for Cognitive Science, TU Darmstadt（图恩-达姆斯塔德大学认知科学中心）；Hessian Center for Artificial Intelligence (Hessian.AI), Darmstadt（黑森人工智能中心（Hessian.AI），达姆斯塔特）
External Photoreflective Tactile Sensing Based on Surface Deformation Measurement
Graduate School of Engineering Science, The University of Osaka（大阪大学工学研究科）；Department of Mechanical Engineering and Science, Kyoto University（京都大学机械工学科）；Faculty of Design Department of Media Design, Kyushu University（九州大学设计学部媒体设计学科）
