Collaborative space robots are an emerging technology with high impact as robots facilitate servicing functions in collaboration with astronauts with higher precision during lengthy tasks, under tight operational schedules, with less risk and costs, making them more efficient and economically more viable. However, human-robot collaboration in space is still a challenge concerning key issues in human-robot interaction, including mobility and collaborative manipulation of objects on a microgravity environment. In this paper we formulate an algorithm that enables a free-flyer robot, equipped with a manipulator, to perform an object handover between a human and a free-flyer robot, in a microgravity environment. To validate and evaluate this algorithm, we present a systematic user study with the goal of understanding the subjective outcome effects of a rigid and compliant impedance robot behavior during the interaction. The results showed that the rigid behavior was overall more preferable and registered higher transfer success during the tasks.
Adaptive vs. Static Robot-to-Human Handover: A Study on Orientation and Approach Direction
Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia（工程学院"Enzo Ferrari"，摩德纳和雷吉奥艾米利亚大学）；Department of Surgery, Medicine, Dentistry and Morphological Sciences, University of Modena and Reggio Emilia（外科、医学、牙科和形态学科学学院，摩德纳和雷吉奥艾米利亚大学）
We Choose to Go to Space: Agent-driven Human and Multi-Robot Collaboration in Microgravity
Beijing University of Posts and Telecommunications（北京邮电大学）；TongJi University（同济大学）；Beihang University（北京航空航天大学）；Beijing Normal University（北京师范大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Carnegie Mellon University（卡内基梅隆大学）
Tampere University（坦佩雷大学）；Queen Mary University of London（伦敦玛丽女王大学）
Human-robot object handover is a key skill for the future of human-robot collaboration. CORSMAL 2020 Challenge focuses on the perception part of this problem: the robot needs to estimate the filling mass of a container held by a human. Although there are powerful methods in image processing and audio processing individually, answering such a problem requires processing data from multiple sensors together. The appearance of the container, the sound of the filling, and the depth data provide essential information. We propose a multi-modal method to predict three key indicators of the filling mas…
Effective human-robot collaboration requires informed anticipation. The robot must anticipate the human's actions, but also react quickly and intuitively when its predictions are wrong. The robot must plan its actions to account for the human's own plan, with the knowledge that the human's behavior will change based on what the robot actually does. This cyclical game of predicting a human's future actions and generating a corresponding motion plan is extremely difficult to model using standard techniques. In this work, we describe a novel Model Predictive Control (MPC)-based framework for find…
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）
STL-Based Motion Planning and Uncertainty-Aware Risk Analysis for Human-Robot Collaboration with a Multi-Rotor Aerial Vehicle
Department of Cybernetics, Czech Technical University in Prague（捷克技术大学布拉格分校控制学系）；Department of Power Generation Technologies and Materials, Ricerca sul Sistema Energetico S.p.A.（能源系统研究股份有限公司电力生成技术与材料学系）；Robotics and Mechatronics Department, Electrical Engineering, Mathematics, and Computer Science Faculty, University of Twente（代尔夫特理工大学机器人与机电学系，电气工程、数学与计算机科学学院）；Department of Computer, Control and Management Engineering, Sapienza University of Rome（罗马萨皮恩扎大学计算机、控制与管理工程系）
Despite great advances in what robots can do, they still experience failures in human-robot collaborative tasks due to high randomness in unstructured human environments. Moreover, a human's unfamiliarity with a robot and its abilities can cause such failures to repeat. This makes the ability to failure explanation very important for a robot. In this work, we describe a user study that incorporated different robotic failures in a human-robot collaboration (HRC) task aimed at filling a shelf. We included different types of failures and repeated occurrences of such failures in a prolonged intera…
Queen Mary University of London（伦敦玛丽女王大学）；University of Technology Sydney（悉尼科技大学）；École polytechnique fédérale de Lausanne（洛桑联邦理工学院）；Worcester Polytechnic Institute（伍斯特理工学院）
Human-robot collaboration requires the contactless estimation of the physical properties of containers manipulated by a person, for example while pouring content in a cup or moving a food box. Acoustic and visual signals can be used to estimate the physical properties of such objects, which may vary substantially in shape, material and size, and also be occluded by the hands of the person. To facilitate comparisons and stimulate progress in solving this problem, we present the CORSMAL challenge and a dataset to assess the performance of the algorithms through a set of well-defined performance …
Bielefeld University of Applied Sciences（比勒费尔德应用科学大学）；Bielefeld University（比勒费尔德大学）
In future, robots will be present in everyday life. The development of these supporting robots is a challenge. A fundamental task for assistance robots is to pick up and hand over objects to humans. By interacting with users, soft factors such as predictability, safety and reliability become important factors for development. Previous works show that collaboration with robots is more acceptable when robots behave and move human-like. In this paper, we present a motion model based on the motion profiles of individual joints. These motion profiles are based on observations and measurements of jo…
Not All Agreement Counts as Corroboration: Provenance-Conserving Multi-View Fusion for Typed Action Admission in Human-Robot Collaboration
McGill University（麦吉尔大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Mila – Quebec AI Institute（米拉-魁北克人工智能研究所）；Norwegian University of Science and Technology（挪威科技大学）；University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）；NVIDIA Corporation（英伟达公司）；University of British Columbia（不列颠哥伦比亚大学）
Object handover is a common human collaboration behavior that attracts attention from researchers in Robotics and Cognitive Science. Though visual perception plays an important role in the object handover task, the whole handover process has been specifically explored. In this work, we propose a novel rich-annotated dataset, H2O, for visual analysis of human-human object handovers. The H2O, which contains 18K video clips involving 15 people who hand over 30 objects to each other, is a multi-purpose benchmark. It can support several vision-based tasks, from which, we specifically provide a base…
The car-to-driver handover is a critically important component of safe autonomous vehicle operation when the vehicle is unable to safely proceed on its own. Current implementations of this handover in automobiles take the form of a generic alarm indicating an imminent transfer of control back to the human driver. However, certain levels of vehicle autonomy may allow the driver to engage in other, non-driving related tasks prior to a handover, leading to substantial difficulty in quickly regaining situational awareness. This delay in re-orientation could potentially lead to life-threatening fai…
Mitigating Compensatory Movements in Prosthesis Users via Adaptive Collaborative Robotics
Human-Robot Interfaces and Interaction, Istituto Italiano di Tecnologia（人机交互与交互，意大利技术研究院）；Dept. of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa（信息学、生物工程、机器人与系统工程系，热那亚大学）
Human-robot handover is a fundamental yet challenging task in human-robot interaction and collaboration. Recently, remarkable progressions have been made in human-to-robot handovers of unknown objects by using learning-based grasp generators. However, how to responsively generate smooth motions to take an object from a human is still an open question. Specifically, planning motions that take human comfort into account is not a part of the human-robot handover process in most prior works. In this paper, we propose to generate smooth motions via an efficient model-predictive control (MPC) framew…
Osaka University（大阪大学）；National Inst. of AIST（国立先进工业科学技术研究所）
In this paper, we present a planner for a master dual-arm robot to manipulate tethered tools with an assistant dual-arm robot's help. The assistant robot provides assistance to the master robot by manipulating the tool cable and avoiding collisions. The provided assistance allows the master robot to perform tool placements on the robot workspace table to regrasp the tool, which would typically fail since the tool cable tension may change the tool positions. It also allows the master robot to perform tool handovers, which would normally cause entanglements or collisions with the cable and the e…
LiHRA: A LiDAR-Based HRI Dataset for Automated Risk Monitoring Methods
Proximity Robotics & Automation GmbH（近距机器人与自动化有限公司）；Institute of Industrial Automation and Software Engineering, University of Stuttgart（工业自动化与软件工程学院，斯图加特大学）
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）
Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes
Centre for Intelligent Sensing, Queen Mary University of London（智能感知中心，伦敦女王玛丽大学）；Idiap Research Institute and École Polytechnique Fédérale de Lausanne（IDiap研究机构和日内瓦联邦理工学院）
We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. To perform the tasks successfully and effectively, the robots have to adopt intelligent collaboration strategies, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability informati…
