Physical Human-Robot Interaction

The REACH lab is involved in a number of projects related to physical human-robot interaction. We have investigated hybrid actuation approaches and controls for the purposes of achieving higher performance while maintaining safety through a low output impedance. We have also developed methods that enable robots to learn about the constraints of physical tasks by learning from demonstrations using instrumented tools (e.g., tongs with force-torque sensors to measure interaction wrenches). As part of a NASA University Leadership Initiative project, we have developed shared autonomy methods where an operator provides informed real-time corrections to the robot while it completes contact-rich tasks, such as sanding or polishing. The research results have applications in industrial manufacturing and novice interactions with robots.

Faculty:

Michael Zinn – Department of Mechanical Engineering, University of Wisconsin–Madison
Bilge Mutlu – Department of Computer Sciences, University of Wisconsin – Madison
Michael Gleicher – Department of Computer Sciences, University of Wisconsin – Madison
Robert Radwin – Department of Industrial and Systems Engineering, University of Wisconsin – Madison

Collaborator(s):

Rich Gardner – Boeing Research & Technology
Chris Reid – Boeing Research & Technology

Students:

Funding:

National Science Foundation (NSF)
NASA University Leadership Initiative (ULI)
Wisconsin Alumni Research Foundation (WARF)

Shared Autonomy

We have developed methods that allow a person to share autonomy with robots completing physical tasks.

Motor Skills Assessment

We are developing robot methods to evaluate motor skills with a focus on autism spectrum disorder.

Macro-Mini Actuation

Our lab has developed macro-mini actuation approaches that allow high-force and high-power manipulators.

Instrumented Tongs

We develop Learning from Demonstration (LfD) interfaces that enable capture of interaction wrenches.

Publications

Senft, E., M. Hagenow, P. Praveena, R. Radwin, M. Zinn, M. Gleicher, and B. Mutlu. “A Method For Automated Drone Viewpoints to Support Remote Robot Manipulation”. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 7704-11.