Robot-assisted dressing offers an opportunity to benefit the lives of many people with disabilities, such as some older adults. However, robots currently lack common sense about the physical implications of their actions on people. The physical implications of dressing are complicated by non-rigid garments, which can result in a robot indirectly applying high forces to a person's body. We present a deep recurrent model that, when given a proposed action by the robot, predicts the forces a garment will apply to a person's body. We also show that a robot can provide better dressing assistance by using this model with model predictive control. The predictions made by our model only use haptic and kinematic observations from the robot's end effector, which are readily attainable. Collecting training data from real world physical human-robot interaction can be time consuming, costly, and put people at risk. Instead, we train our predictive model using data collected in an entirely self-supervised fashion from a physics-based simulation. We evaluated our approach with a PR2 robot that attempted to pull a hospital gown onto the arms of 10 human participants. With a 0.2s prediction horizon, our controller succeeded at high rates and lowered applied force while navigating the garment around a persons fist and elbow without getting caught. Shorter prediction horizons resulted in significantly reduced performance with the sleeve catching on the participants' fists and elbows, demonstrating the value of our model's predictions. These behaviors of mitigating catches emerged from our deep predictive model and the controller objective function, which primarily penalizes high forces.
Force-Modulated Visual Policy for Robot-Assisted Dressing with Arm Motions
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Department of Engineering Design, Indian Institute of Technology, Madras（印度理工学院Madras分校工程设计系）
Learning-based Force Sensing and Impedance Matching for Safe Haptic Feedback in Robot-assisted Laparoscopic Surgery
organization= Surgical Robotics Laboratory (SRL), Mechanical Engineering Department, Concordia University , city= Montreal , state= QC , country= Canada；Robotics (SuPER) Centre, Department of Surgery, McGill University , city= Montreal , state= QC , country= Canada；Joyce Posluns Centre for Image Guided Innovation \& Therapeutic Intervention (PCIGITI), The Hospital for Sick Children (SickKids) , city= Toronto , state= ON , country= Canada
Symbolic Runtime Verification and Adaptive Decision-Making for Robot-Assisted Dressing
Department of Computer Science（计算机科学系）；University of Manchester（曼彻斯特大学）；University of York（约克大学）；School of Electrical and Electronic Engineering（电子与电气工程学院）；The University of Sheffield（谢菲尔德大学）；School of Computer Science（计算机科学学院）
Therapist-Robot-Patient Physical Interaction is Worth a Thousand Words: Enabling Intuitive Therapist Guidance via Remote Haptic Control
Department of Cognitive Robotics, Delft University of Technology（认知机器人系，代尔夫特理工大学）；Department of Rehabilitation Medicine, Erasmus Medical Center（康复医学系，埃拉斯姆斯医疗中心）
Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted Surgery Using Structured Light
School of Electronic Information Engineering, Tongji University（同济大学电子与信息工程学院）；MicroPort MedBot (Group) Company Ltd.（微创医疗机器人（集团）股份有限公司）；College of Engineering, Peking University（北京大学工学院）；City University of Hong Kong（香港城市大学）
Haptic Guidance and Haptic Error Amplification in a Virtual Surgical Robotic Training Environment
Stanford University（斯坦福大学）；Hospital for Special Surgery（特种外科医院）；University of Notre Dame（圣母大学）；University of Washington（华盛顿大学）；Ben-Gurion University of the Negev（内盖夫本-古里安大学）；Intuitive Surgical Inc.（直觉外科公司）；Case Western Reserve University（凯斯西储大学）
Task Dynamics of Prior Training Influence Visual Force Estimation Ability During Teleoperation
Stanford University（斯坦福大学）；Palo Alto Veterans Affairs Health Care System（帕洛阿尔托退伍军人事务医疗系统）；Intuitive Surgical, Inc.（直觉外科公司）；Ben-Gurion University of the Negev（内盖夫本-古里安大学）
Shaft-integrated Force Sensing with Transformer-based Dynamics Compensation for Telesurgery
Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University（电气、计算机与系统工程系，凯斯西储大学）；Department of Mechanical and Aerospace Engineering, Case Western Reserve University（机械与航空航天工程系，凯斯西储大学）；Resense GmbH；Semantic Information Systems Group, Osnabrück University（语义信息系统组，奥斯纳布吕克大学）；Justus Liebig University（吉森大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
Real-Time Instrument Segmentation in Robotic Surgery using Auxiliary Supervised Deep Adversarial Learning
NUS Graduate School for Integrative Sciences and Engineering (NGS)（新加坡国立大学综合科学与工程研究生院（NGS））；National University of Singapore（新加坡国立大学）；University of Moratuwa（莫拉图瓦大学）；NIT Trichy（印度国立理工学院蒂鲁吉拉伯利分校）
Shared-Control Teleoperation Paradigms on a Soft Growing Robot Manipulator
Kadir Has University（卡迪尔哈斯大学）；University of Naples Federico II（那不勒斯费德里科二世大学）；Stanford University（斯坦福大学）；Washington State University（华盛顿州立大学）；Purdue University（普渡大学）；UC Santa Barbara（加州大学圣巴巴拉分校）
