Mechatronics and Robotics Research Lab

Optimizing Human Performance through Mechatronics

The Mechatronics and Robotics Research Laboratory at the University of Massachusetts Amherst in the Department of Mechanical & Industrial Engineering. The lab is directed by Professor Frank Sup. We focus on the advancement of physical human-machine interaction. The core of the lab’s research is human-centered mechatronic design in developing collaborative and rehabilitative technologies. The research topics include wearable robotics, such as intelligent prosthetics and exoskeletons, human motion simulation and prediction, and advanced design and control structures and methodologies. We strive to expand the boundaries in the research community while providing a hands-on learning environment in leading-edge science.

Interested in Working with the Lab? Click here to submit your information

Highlighted Research Projects

Leveraging Fluid Structure Interaction principles to design underwater assistive gait systems

Using bioelectrical signals, like EMG, and motion to continuously predict future human joint movements for robotic teleoperation

Creating the groundwork for modularized robotic educational tools that nursing and engineering students can use to increase familiarity with robots that may be encountered in the workplace

Focused on creating sensors for measuring pressures and forces for interactions with soft bodies

Developing advanced methods for capturing human and robotic motion and new technologies for recording human biomechanics.

Predicting optimal behavior of wearable robotics to optimize the user’s performance, efficiency, and loading of their limbs based on their individual constraints and needs

Latest New from the MRRL

Past News from the MRRL

August 31, 2023

IEEE Spectrum Video Friday highlighted the collaborative work between the MRRL and FSI Labs on campus that are working to understand how induced vortices can be used to drive downstream motion in a fluid flow. The work is movitaved by applications in underwater assisted gait.

July 25, 2023

This paper reviews model-based and model-free approaches for continuous prediction of human joint motion using surface electromyography (EMG) signals. The review focuses on approaches that solve the regression problem of continuously decoding motion from EMG as opposed to classification or discrete gesture identification methods. Model-based approaches use physics-based models to capture the dynamics of the muscles; and musculoskeletal geometry to estimate muscle forces and joint torques. Model-free machine learning and deep learning architectures employ training to learn the statistical

January 15, 2023

The overall goal of this research thrust is to establish new and fundable interdisciplinary collaborations amongst UMASS faculty that advance human-centered robotics. This will be achieved by hosting a series of activities that facilitate interactions and foster collaboration opportunities between UMass Amherst faculty and student researchers from robotics, human neuromechanics, and human wearable technology.