A dynamic autonomy allocation framework automatically shifts how much control lies with the human versus the robotics autonomy, for example based on factors such as environmental safety or user preference. To investigate the question of which factors should drive dynamic autonomy allocation, we perform a human subject study to collect ground truth data that shifts between levels of autonomy during shared-control robot operation. Information streams from the human, the interaction between the human and the robot, and the environment are analyzed. Machine learning methods -- both classical and deep learning -- are trained on this data. An analysis of information streams from the human-robot team suggests features which capture the interaction between the human and the robotics autonomy are the most informative in predicting when to shift autonomy levels. Even the addition of data from the environment does little to improve upon this predictive power. The features learned by deep networks, in comparison to the hand-engineered features, prove variable in their ability to represent shift-relevant information. This work demonstrates the classification power of human-only and human-robot interaction information streams for use in the design of shared-control frameworks, and provides insights into the comparative utility of various data streams and methods to extract shift-relevant information from those data.
Legible Shared Autonomy: Implicit Communication of Robot Belief through Motion
Department of Automation, University of Science and Technology of China（自动化系，中国科学技术大学）；Department of Automation, Hefei University of Technology（自动化系，合肥工业大学）；National Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）
From Novice to Skilled: RL-based Shared Autonomy Communicating with Pilots in UAV Multi-Task Missions
Institute for Clarity in Documentation（文献清晰度研究所）；The Thørväld Group（索瓦尔德集团）；Inria Paris-Rocquencourt（法国国家信息与自动化研究所巴黎-罗康校区）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）；The Kumquat Consortium（金桔联盟）
"I am the follower, also the boss": Exploring Different Levels of Autonomy and Machine Forms of Guiding Robots for the Visually Impaired
Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
DTRT: Enhancing Human Intent Estimation and Role Allocation for Physical Human-Robot Collaboration
CAS Engineering Laboratory for Intelligent Industrial Vision, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所CAS工程视觉实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Toward a Science of Autonomy for Physical Systems: Paths
University of California, Berkeley（加州大学伯克利分校）；Johns Hopkins University（约翰斯·霍普金斯大学）；Massachusetts Institute of Technology（麻省理工学院）；Computing Community Consortium（计算社区联盟）
Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments
University of Electronic Science and Technology of China（电子科技大学）；University of Macau（澳门大学）；Macau University of Science and Technology（澳门科技大学）；Tsinghua University（清华大学）
Conjugated Capabilities: Interrelations of Elementary Human Capabilities and Their Implication on Human-Machine Task Allocation and Capability Testing Procedures
Chair of Mechatronics, University of Augsburg（奥格斯堡大学机电学系）；Institute for Quality Assurance in Prevention and Rehabilitation (iqpr), German Sport University Cologne（预防与康复质量保证研究所（iqpr），德国体育大学科隆）；Department of Balanced HSI at Fraunhofer FKIE（Fraunhofer FKIE的平衡HSI部门）；Institute of Industrial Engineering & Ergonomics at RWTH Aachen University（亚琛工业大学工业工程与人因学研究所）
Human-Robot Team Performance Compared to Full Robot Autonomy in 16 Real-World Search and Rescue Missions: Adaptation of the DARPA Subterranean Challenge
MOSAIC: Modular Scalable Autonomy for Intelligent Coordination of Heterogeneous Robotic Teams
FZI Forschungszentrum für Informatik, Karlsruhe, Germany（弗赖堡信息研究中心，卡尔斯鲁厄，德国）；Robotic Systems Lab (RSL), ETH Zürich, Zürich, Switzerland（机器人系统实验室（RSL），苏黎世联邦理工学院，苏黎世，瑞士）；Machine Intelligence and Robotics Lab (MaiRo), Karlsruhe Institute for Technology (KIT), Karlsruhe, Germany（机器智能与机器人实验室（MaiRo），卡尔斯鲁厄技术学院（KIT），卡尔斯鲁厄，德国）；European Space Agency (ESA), Noordwijk, Netherlands（欧洲航天局（ESA），诺德威克，荷兰）
A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（乔治亚理工学院机器人与智能机器研究所）；GRASP Laboratory, University of Pennsylvania（宾夕法尼亚大学GRASP实验室）；Combat Capabilities Development Command, Army Research Laboratory (CCDC ARL)（陆军研究实验室作战能力发展司令部（CCDC ARL））
Census-Based Population Autonomy For Distributed Robotic Teaming
Department of Mechanical Engineering, Massachusetts Institute of Technology（麻省理工学院机械工程系）；Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）；Sibley School of Mechanical and Aerospace Engineering, Cornell University（康奈尔大学Sibley机械与航空航天工程学院）
