Mike Hagenow

About

Mike Hagenow is an assistant professor in the Department of Computer Sciences at UW-Madison. His research focuses on developing methods for effective human-robot teaming, particularly in contact-rich tasks that are physically demanding on people. His main research interests include shared autonomy and robot learning. Prior to his current position, he was a postdoctoral fellow in the Aeronautics and Astronautics department and CSAIL at MIT. Hagenow holds a BS in mechanical engineering from Tufts University, obtained in 2014, and both an MS and PhD in mechanical engineering from UW-Madison, completed in 2019 and 2023 respectively.

Research topics

• Computer Science
• Artificial Intelligence
• Human–computer interaction

Selected publications

• Task-Level Authoring for Remote Robot Teleoperation

  Frontiers in Robotics and AI · 2021 · 20 citations

  Remote teleoperation of robots can broaden the reach of domain specialists across a wide range of industries such as home maintenance, health care, light manufacturing, and construction. However, current direct control methods are impractical, and existing tools for programming robot remotely have focused on users with significant robotic experience. Extending robot remote programming to end users, i.e., users who are experts in a domain but novices in robotics, requires tools that balance the r…

  DOI

• Human–Robot Collaboration With a Corrective Shared Controlled Robot in a Sanding Task

  Human Factors The Journal of the Human Factors and Ergonomics Society · 2024-08-08 · 6 citations

  article

  OBJECTIVE: Physical and cognitive workloads and performance were studied for a corrective shared control (CSC) human-robot collaborative (HRC) sanding task. BACKGROUND: Manual sanding is physically demanding. Collaborative robots (cobots) can potentially reduce physical stress, but fully autonomous implementation has been particularly challenging due to skill, task variability, and robot limitations. CSC is an HRC method where the robot operates semi-autonomously while the human provides real-ti…

  PublisherDOI

• Coordinated Multi-Robot Shared Autonomy Based on Scheduling and Demonstrations

  IEEE Robotics and Automation Letters · 2023-10-25 · 6 citations

  article1st authorCorresponding

  Shared autonomy methods, where a human operator and a robot arm work together, have enabled robots to complete a range of complex and highly variable tasks. Existing work primarily focuses on one human sharing autonomy with a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single</i> robot. By contrast, in this letter we present an approach for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi…

  PublisherDOI

• A Method For Automated Drone Viewpoints to Support Remote Robot Manipulation

  2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2022-10-23 · 6 citations

  article

  Drones can provide a minimally-constrained adapting camera view to support robot telemanipulation. Furthermore, the drone view can be automated to reduce the burden on the operator during teleoperation. However, existing approaches do not focus on two important aspects of using a drone as an automated view provider. The first is how the drone should select from a range of quality viewpoints within the workspace (e.g., opposite sides of an object). The second is how to compensate for unavoidable…

  PublisherDOI

• Assessing the Perceived Realism of Kinesthetic Haptic Renderings Under Parameter Variations

  2022-03-21 · 3 citations

  article

  Despite the large amount of research on kinesthetic haptic devices and haptic effect modeling, there is limited work assessing the perceived realism of kinesthetic model renderings. Identifying the impact of haptic effect parameters in perceived realism can help to inform the required accuracy of kinesthetic renderings. In this work, we model common kinesthetic haptic effects and evaluate the perceived realism of varying model parameters via a user study. Our results suggest that parameter accur…

  PublisherDOI

Frequent coauthors

• Michael Zinn

  University of Wisconsin–Madison

  23 shared

• Michael Gleicher

  University of Wisconsin–Madison

  22 shared

• Bilge Mutlu

  20 shared

• Robert G. Radwin

  University of Wisconsin–Madison

  15 shared

• Emmanuel Senft

  Idiap Research Institute

  13 shared

• Nitzan Orr

  University of Wisconsin–Madison

  4 shared

• Evan Laske

  3 shared

• Kimberly Hambuchen

  Johnson Space Center

  3 shared

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