Interactive & Emergent Autonomy Lab

Projects

Active Learning and Data-Driven Control Active Perception in Human-Swarm Collaboration Algorithmic Matter and Emergent Computation Control for Nonlinear and Hybrid Systems Cyber Physical Systems in Uncertain Environments Haptic Languages Information Maximizing Clinical Diagnostics Reactive Learning in Underwater Exploration Robot-Assisted Rehabilitation Software-Enabled Biomedical Devices Vibrissal Responsive Neurons

Software-Enabled Biomedical Devices

Ekso

A lower-limb Ekso Bionics exoskeleton

Lunar Lander

Simulated lunar lander

Human-Machine Interaction

Human-machine interaction


Assistive devices are intended to enable users to learn motion in a safe and effective manner or to accomplish tasks they wouldn't be able to perform on their own (e.g. due to injury or degeneration). In this work we develop algorithms for task-specific support of motion, specifically considering dynamic tasks, where reaction time is critical to the success of the task. We aim to create software that ensures efficacy and safety, while leaving room for the user to be in control and/or exert effort. We test our algorithms in three different experimental testbeds:


• virtual environments, such as a simulated car or Lunar Lander,
• an upper-limb assistive device (NACT-3D),
• and a lower-limb Ekso Bionics exoskeleton.

People

Tommy Berrueta (Ph.D. Student)
Katie Fitzsimons (Ph.D. Student)
Ola Kalinowska (Ph.D. Student)
Milli Schlafly (Ph.D. Student)

Collaborators

Brenna Argall, Northwestern University
Julius Dewald, Northwestern University
Ekso Bionics

Publications

Task-based hybrid shared control for training through forceful interaction
K. Fitzsimons, A. Kalinowska, J. Dewald and T. D. Murphey
International Journal of Robotics Research, 2020. PDF

Ergodicity reveals assistance and learning in physical human robot interaction
K. Fitzsimons, A. M. Acosta, J. Dewald, and T. D. Murphey
Science Robotics, vol. 4, no. 29, 2019.

Dynamical system segmentation for information measures in motion
T. Berrueta, A. Pervan, K. Fitzsimons, and T. Murphey
IEEE Robotics and Automation Letters, vol. 4, no. 1, pp. 169–176, 2019. PDF

Data-driven gait segmentation for walking assistance in a lower-limb assistive device
A. Kalinowska, T. Berrueta, A. Zoss, and T. D. Murphey
IEEE Int. Conf. on Robotics and Automation (ICRA), 2019. PDF

Operation and imitation under safety-aware shared control
A. Broad, T. Murphey, and B. Argall
Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018. PDF

Online user assessment for minimal intervention during task-based robotic assistance
A. Kalinowska, K. Fitzsimons, J. Dewald, and T. D. Murphey
Robotics: Science and Systems Proceedings, 2018. PDF

Funding

This project is funded by the National Science Foundation–National Robotics Initiative: Task-Based Assistance for Software-Enabled Biomedical Devices