October 29, 2021 11:00am-12:30pm

Location: Pettit Microelectronics Building, 102A&B Conference Room


Lower-body exoskeletons and prostheses have the potential of restoring autonomy to millions of individuals with ambulatory disabilities, and thereby improving quality of life. However, achieving locomotive stability is challenging enough from a robotics and control perspective, let alone addressing the added complexity of satisfying subjective gait preferences of a human user. Thus, the goal of this talk is to discuss how theoretic approaches to bipedal locomotion, based upon nonlinear controllers with formal guarantees of stability, can be coupled with learning to achieve user-preferred stable locomotion.

In my talk, I will first discuss the theoretical underpinnings of achieving provably stable locomotion via nonlinear control theory, and apply this methodology to various dynamic robotic platforms experimentally. Then, I explore the unification of preference-based learning with this formal approach to locomotion to explicitly optimize user preference and comfort. Finally, I experimentally demonstrate the combined framework on a full lower-body exoskeleton, with both non-disabled subjects and subjects with complete motor paraplegia. This work, therefore, demonstrates the utility of coupling preference-based learning with control theory in a structured fashion with a view towards practical application. Ultimately, this result provides a formal approach for achieving locomotive autonomy with robotic assistive devices that has the potential to accelerate clinical implementation and enable the use of these devices in everyday life.


Maegan Tucker is currently a PhD Candidate in the Mechanical and Civil Engineering Department at the California Institute of Technology (Caltech). She received her Bachelor of Science in Mechanical Engineering from Georgia Tech in 2017. Her research is centered around developing systematic methods of achieving stable, robust, and natural bipedal locomotion on lower-body assistive devices, as well as developing human-in-the-loop methods to customize the experimental locomotion based on subjective user feedback.

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