Mathematical Models of Adaptation in Human-Robot Collaboration
The Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Human-Computer Interaction Institute, Carnegie Mellon University（卡内基梅隆大学人机交互研究所）；Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
Stefanos Nikolaidis, Jodi Forlizzi, David Hsu, Julie Shah, Siddhartha Srinivasa
A robot operating in isolation needs to reason over the uncertainty in its model of the world and adapt its own actions to account for this uncertainty. Similarly, a robot interacting with people needs to reason over its uncertainty over the human internal state, as well as over how this state may change, as humans adapt to the robot. This paper summarizes our own work in this area, which depicts the different ways that probabilistic planning and game-theoretic algorithms can enable such reasoning in robotic systems that collaborate with people. We start with a general formulation of the problem as a two-player game with incomplete information. We then articulate the different assumptions within this general formulation, and we explain how these lead to exciting and diverse robot behaviors in real-time interactions with actual human subjects, in a variety of manufacturing, personal robotics and assistive care settings.
Towards Real-time Adaptation of Embodied Agent in Human-Robot Collaboration
University of Southern California Department of Electrical（南加州大学电气与计算机工程系）；University of Southern California Department of Computer Science Los Angeles CA USA（南加州大学计算机科学系洛杉矶加州美国）；University of Southern California（南加州大学）
Computational ergonomics for task delegation in Human-Robot Collaboration: spatiotemporal adaptation of the robot to the human through contactless gesture recognition
Systematic Adaptation of Communication-focused Machine Learning Models from Real to Virtual Environments for Human-Robot Collaboration
School of Engineering（工程学院）；The University of British Columbia（不列颠哥伦比亚大学）；Department of Aerospace Engineering（航空航天工程系）；Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）；Department of Mechanical Engineering（机械工程系）；University of Victoria（维多利亚大学）
Human-Like Robot Impedance Regulation Skill Learning from Human-Human Demonstrations
Department of Mechanical and Automation Engineering, T-Stone Robotics Institute, The Chinese University of Hong Kong（机械与自动化工程系，T-Stone机器人研究院，香港中文大学）；College of Science and Technology, Ningbo University（科学与技术学院，宁波大学）；Idiap Research Institute（Idiap研究机构）
TATIC: Task-Aware Temporal Learning for Human Intent Inference from Physical Corrections in Human-Robot Collaboration
Department of Mechanical Engineering, Texas A&M University（机械工程系，德克萨斯农工大学）；Department of Civil and Environmental Engineering, Texas A&M University（土木与环境工程系，德克萨斯农工大学）
