Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for grasping. However, for robotic systems to attain a level of proficiency comparable to humans, especially in consistently handling and manipulating unfamiliar objects, integrating artificial tactile sensing is increasingly essential. We introduce a novel physics-informed, data-driven approach to detect slip continuously in real time. We employ the GelSight Mini, an optical tactile sensor, attached to custom-designed grippers to gather tactile data. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinctive features and formulates slip detection as a classification problem. To evaluate our approach, we test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials. Our results show that the best classification algorithm achieves a high average accuracy of 95.61%. We further illustrate the practical application of our research in dynamic robotic manipulation tasks, where our real-time slip detection and prevention algorithm is implemented.
Guangdong University of Technology（广东工业大学）；South China University of Technology（华南理工大学）
Humans can accurately determine whether the object in hand has slipped or not by visual and tactile perception. However, it is still a challenge for robots to detect in-hand object slip through visuo-tactile fusion. To address this issue, a novel visuo-tactile fusion deep neural network is proposed to detect slip, which is a time-dependent continuous action. By using the multi-scale temporal convolution network (MS-TCN) to extract the temporal features of visual and tactile data, the slip can be detected effectively. In this paper, a 7-dregree-of-freedom (7-DoF) robot manipulator equipped with…
Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT（麻省理工学院计算机科学与人工智能实验室（CSAIL））
Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community. In this paper, we propose a new method based on deep neural network (DNN) to detect slip. The training data is acquired by a GelSight tactile sensor and a camera mounted on a gripper when we use a robot arm to grasp and lift 94 daily objects with different grasping forces and grasping positions. The DNN is trained to classify whether a slip occurred or not. To evaluate the performance of the DNN, we test 10 unseen objects in 152 grasps. A detection accuracy as high as …
Contact Status Recognition and Slip Detection with a Bio-inspired Tactile Hand
School of Mechanical Engineering, Southeast University Nanjing 211189, China；School of Electronic Science \& Engineering, Southeast University Nanjing 211189, China
Agile Robots AG（敏捷机器人公司）；Technische Universität München（慕尼黑工业大学）；Universität Hamburg（汉堡大学）
An unstable grasp pose can lead to slip, thus an unstable grasp pose can be predicted by slip detection. A regrasp is required afterwards to correct the grasp pose in order to finish the task. In this work, we propose a novel regrasp planner with multi-sensor modules to plan grasp adjustments with the feedback from a slip detector. Then a regrasp planner is trained to estimate the location of center of mass, which helps robots find an optimal grasp pose. The dataset in this work consists of 1 025 slip experiments and 1 347 regrasps collected by one pair of tactile sensors, an RGB-D camera and …
CLTP: Contrastive Language-Tactile Pre-training for 3D Contact Geometry Understanding
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beihang University（北航）；Beijing University of Posts and Telecommunications（北京邮电大学）；Samsung Research China（三星中国研究院）
HiTac-WAM: A Hierarchical Tactile World Action Model for Contact-Rich Robot Manipulation
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（北京人工智能研究院）
Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation
Shanghai Key Lab of Intelligent Information Processing, College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院智能信息处理上海市重点实验室）；College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学智能机器人与先进制造学院）；Lenovo CTO Organization（联想首席技术官组织）；Nanyang Technological University（南洋理工大学）；TeleAl, China Telecom（中国电信天翼人工智能公司）
Technische Universität Darmstadt（达姆施塔特工业大学）；Max-Planck-Institut für Intelligente Systeme（马克斯·普朗克智能系统研究所）
Currently grip control during in-hand manipulation is usually modeled as part of a monolithic task, yielding complex controllers based on force control specialized for their situations. Such non-modular and specialized control approaches render the generalization of these controllers to new in-hand manipulation tasks difficult. Clearly, a grip control approach that generalizes well between several tasks would be preferable. We propose a modular approach where each finger is controlled by an independent tactile grip controller. Using signals from the human-inspired biotac sensor, we can predict…
This paper develops closed-loop tactile controllers for dexterous robotic manipulation with a dual-palm robotic system. Tactile dexterity is an approach to dexterous manipulation that plans for robot/object interactions that render interpretable tactile information for control. We divide the role of tactile control into two goals: 1) control the contact state between the end-effector and the object (contact/no-contact, stick/slip) by regulating the stability of planned contact configurations and monitoring undesired slip events; and 2) control the object state by tactile-based tracking and ite…
Cross-Modal Visuo-Tactile Representation Learning with Action Chunking Transformers for Contact-Rich Manipulation
Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；Innovation and Research and Development Department, BoardWare Information System Company Ltd.（创新与研发部，BoardWare信息系统有限公司）；School of Artificial Intelligence, Nanjing Agricultural University（人工智能学院，南京农业大学）；School of Computing, National University of Singapore（计算机学院，新加坡国立大学）；School of Electronic Engineering and Automation, Guilin University of Electronic Technology（电子工程与自动化学院，桂林电子科技大学）
OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation
TARS Robotics（TARS机器人实验室）；National University of Singapore（新加坡国立大学）；Fudan University（复旦大学）；CASIA（中国科学院自动化研究所）；Tsinghua University（清华大学）；Zhongguancun Academy（中关村学院）；Beihang University（北京航空航天大学）
Grasp, Slide, Roll: Comparative Analysis of Contact Modes for Tactile-Based Shape Reconstruction
Amazon Fulfillment Technologies & Robotics（亚马逊履行技术与机器人）；Robotics Institute at Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校Siebel计算与数据科学学院）
Joint estimation of grasped object pose and extrinsic contacts is central to robust and dexterous manipulation. In this paper, we propose a novel state-estimation algorithm that jointly estimates contact location and object pose in 3D using exclusively proprioception and tactile feedback. Our approach leverages two complementary particle filters: one to estimate contact location (CPFGrasp) and another to estimate object poses (SCOPE). We implement and evaluate our approach on real-world single-arm and dual-arm robotic systems. We demonstrate that by bringing two objects into contact, the robot…
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 uncertainties and gain information about the local physical interactions between the in-contact objects and the robot, that are often not accessible via remote sensing. Tactile sensors can be grouped into two main categories: electronic tactile skins and camera based optical tactile sensors. The former are slim and can be fitted to different body parts, whereas th…
Perception and Robotics Group, University of Maryland, College Park（马里兰大学帕克分校感知与机器人小组）；University of Maryland, College Park（马里兰大学帕克分校）；University of Maryland（马里兰大学）
Grasping and manipulating a wide variety of objects is a fundamental skill that would determine the success and wide spread adaptation of robots in homes. Several end-effector designs for robust manipulation have been proposed but they mostly work when provided with prior information about the objects or equipped with external sensors for estimating object shape or size. Such approaches are limited to many-shot or unknown objects and are prone to estimation errors from external estimation systems. We propose an approach to grasp and manipulate previously unseen or zero-shot objects: the object…
DexViTac: Collecting Human Visuo-Tactile-Kinematic Demonstrations for Contact-Rich Dexterous Manipulation
State Key Laboratory of Intelligent Manufacturing Equipment and Technology（智能制造装备与技术国家重点实验室）；Huazhong University of Science and Technology（华中科技大学）；Wuhan Huaweike Intelligent Technology Co., Ltd.（武汉华为凯科技有限公司）
TACTIC: Understanding Tactile Encoders and Conditioning for Contact-rich Robot Manipulation Policies
University of Technology Nuremberg（纽伦堡工业大学）；Ludwig-Maximilians University Munich（慕尼黑路德维希-马克西米利安大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Deggendorf Institute of Technology（德根多夫理工学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；University of Tromso（特罗姆瑟大学）；DLR-German Aerospace Center（德国航空航天中心）
Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
Peking University（北京大学）；The Hong Kong University of Science and Technology（香港科技大学）；Nanjing University（南京大学）；State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）
We study gravitational pivoting, a constrained version of in-hand manipulation, where we aim to control the rotation of an object around the grip point of a parallel gripper. To achieve this, instead of controlling the gripper to avoid slip, we embrace slip to allow the object to rotate in-hand. We collect two real-world datasets, a static tracking dataset and a controller-in-the loop dataset, both annotated with object angle and angular velocity labels. Both datasets contain force-based tactile information on ten different household objects. We train an LSTM model to predict the angular posit…
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）
The ability to perceive object slip via tactile feedback enables humans to accomplish complex manipulation tasks including maintaining a stable grasp. Despite the utility of tactile information for many applications, tactile sensors have yet to be widely deployed in industrial robotics settings; part of the challenge lies in identifying slip and other events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. These sensors have many desirable properties including high durability and reliability, and are built from ine…
Hong Kong University of Science and Technology（香港科技大学）
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 with embedded marker array bonded to rigid frame, and a web camera with a fisheye lens. While being excited with contact force, the camera tracks the movements of markers and deformation field is obtained. Compared to existing tactile sensors, our sensor features compact footprint, high resolution, and ease of fabrication. Besides, utilizing the deformation fie…
National Institute of Standards and Technology（美国国家标准与技术研究院）
The existence of tactile afferents sensitive to slip-related mechanical transients in the human hand augments the robustness of grasping through secondary force modulation protocols. Despite this knowledge and the fact that tactile-based slip detection has been researched for decades, robust slip detection is still not an out-of-the-box capability for any commercially available tactile sensor. This research seeks to bridge this gap with a comprehensive study addressing several aspects of slip detection. Key developments include a systematic data collection process yielding millions of sensory …
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…
University of Bristol（布里斯托大学）；Bristol Robotics Laboratory（布里斯托机器人实验室）；University College Dublin（都柏林大学）
We present a modified TacTip biomimetic optical tactile sensor design which demonstrates the ability to induce and detect incipient slip, as confirmed by recording the movement of markers on the sensor's external surface. Incipient slip is defined as slippage of part, but not all, of the contact surface between the sensor and object. The addition of ridges - which mimic the friction ridges in the human fingertip - in a concentric ring pattern allowed for localised shear deformation to occur on the sensor surface for a significant duration prior to the onset of gross slip. By detecting incipien…
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）
Despite the utility of tactile information, tactile sensors have yet to be widely deployed in industrial robotics settings. Part of the challenge lies in identifying slip and other key events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. Although these sensors have a low resolution, they have many other desirable properties including high reliability and durability, a very slim profile, and a low cost. We are able to achieve slip detection accuracies of greater than 91% while being robust to the speed and direct…
