Queensland University of Technology（昆士兰科技大学）；ARC Centre of Excellence for Robotic Vision（澳大利亚研究理事会机器人视觉卓越中心）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Monash University（莫纳什大学）；University of Birmingham（伯明翰大学）
Valerio Ortenzi, Akansel Cosgun, Tommaso Pardi, Wesley Chan, Elizabeth Croft, Dana Kulic
This article surveys the literature on human-robot object handovers. A handover is a collaborative joint action where an agent, the giver, gives an object to another agent, the receiver. The physical exchange starts when the receiver first contacts the object held by the giver and ends when the giver fully releases the object to the receiver. However, important cognitive and physical processes begin before the physical exchange, including initiating implicit agreement with respect to the location and timing of the exchange. From this perspective, we structure our review into the two main phases delimited by the aforementioned events: 1) a pre-handover phase, and 2) the physical exchange. We focus our analysis on the two actors (giver and receiver) and report the state of the art of robotic givers (robot-to-human handovers) and the robotic receivers (human-to-robot handovers). We report a comprehensive list of qualitative and quantitative metrics commonly used to assess the interaction. While focusing our review on the cognitive level (e.g., prediction, perception, motion planning, learning) and the physical level (e.g., motion, grasping, grip release) of the handover, we briefly discuss also the concepts of safety, social context, and ergonomics. We compare the behaviours displayed during human-to-human handovers to the state of the art of robotic assistants, and identify the major areas of improvement for robotic assistants to reach performance comparable to human interactions. Finally, we propose a minimal set of metrics that should be used in order to enable a fair comparison among the approaches.
Object-Independent Human-to-Robot Handovers using Real Time Robotic Vision
TU Wien（维也纳工业大学）；Australian Center of Robotic Vision（澳大利亚机器人视觉中心）；Monash University（蒙纳士大学）；University of Birmingham（伯明翰大学）；Queensland University of Technology（昆士兰科技大学）
Motion Planning for Robotics: A Review for Sampling-based Planners
Chair of Robotics, Artificial Intelligence and Real-time Systems, TUM School of Computation, Information and Technology (CIT), Technical University of Munich（机器人、人工智能与实时系统教授会，技术大学慕尼黑计算、信息与技术学院）；Human-Robot Interfaces and Interaction(HRI2), Istituto Italiano Di Tecnologia (IIT)（人机接口与交互（HRI2），意大利技术研究所）；Chair of Robotics and Systems Intelligence, Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich（机器人与系统智能教授会，慕尼黑机器人与机器智能研究所，技术大学慕尼黑）；School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；School of Computer Science, University of Nottingham（计算机科学学院，诺丁汉大学）
Task-Agnostic Adaptation for Safe Human-Robot Handover
Carnegie Mellon University（卡内基梅隆大学）
人机交互是提高现代生产线柔性的重要组成部分。然而，在现实世界的应用中，任务（\ie，机器人需要操作的条件，例如环境照明条件、要与之交互的人类主体以及硬件平台）可能会变化，并且在这些变化的任务下最佳地且有效地配置和适配机器人系统仍然具有挑战性。为了应对这一挑战，本文提出了一种与任务无关的自适应控制器，该控制器可以1）适应不同的照明条件，2）适应个人行为并确保与不同人交互时的安全性，3）通过不同的控制接口轻松实现跨机器人平台的传输。建议的框架进行了测试，使用FANUC LR Mate 200 id/7 L机器人和Kinova Gen 3机器人的人机交接任务。实验结果表明，所提出的任务无关控制器可以在不同的任务之间实现一致的性能。
RGB-D Video Generation for Improving Human-to-Robot Object Handover Prediction
Nanyang Technological University（南洋理工大学）；College of Computing and Data Science（计算与数据科学学院）；Zhejiang University（浙江大学）；College of Computer Science and Technology（计算机科学与技术学院）；Alibaba Group（阿里巴巴集团）
Expressing and Inferring Action Carefulness in Human-to-Robot Handovers
Italian Institute of Technology（意大利技术研究院）；University of Genoa（热那亚大学）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Institute for Systems and Robotics (ISR Lisboa)（系统与机器人研究所（里斯本））
Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic Manipulators: Recent Advances in AI, Machine Learning, and Digital Twin
King Fahd University of Petroleum and Minerals（法赫德石油和矿产大学）；Interdisciplinary Research Center for Intelligent Manufacturing and Robotics（智能制造与机器人跨学科研究中心）
A review on cloud robotics based frameworks to solve simultaneous localization and mapping (slam) problem
National Institute of Technology, Raipur（印度国立理工学院赖布尔分校）；Motilal Nehru National Institute of Technology, Allahabad（莫蒂拉尔·尼赫鲁国立理工学院阿拉哈巴德分校）；Indian Institute of Information Technology, Allahabad（印度信息技术学院阿拉哈巴德分校）
Robotic Vision for Human-Robot Interaction and Collaboration: A Survey and Systematic Review
Australian Research Council Centre of Excellence for Robotic Vision（澳大利亚研究理事会机器人视觉卓越中心）；Queensland University of Technology（昆士兰科技大学）；Monash University（莫纳什大学）；University of Oxford（牛津大学）
Robotics Applications in Neurology: A Review of Recent Advancements and Future Directions
Kariadi Hospital（卡里亚迪医院）；Indonesia Center for Technology Empowerment (ICTE)（印度尼西亚技术赋能中心）；Diponegoro University（迪波内戈罗大学）；UNDIP（迪波内戈罗大学）；UNS（塞贝拉斯马雷特大学）；Syiah Kuala University（锡亚库阿拉大学）；RS Permata Cirebon（佩尔马塔井里汶医院）
