Portrait of Audrey Sedal

Audrey Sedal

Associate Academic Member
Assistant Professor, McGill University, Department of Mechanical Engineering
Research Topics
Generative Models
Optimization
Reinforcement Learning
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Google Scholar
LinkedIn

Biography

Audrey Sedal is an assistant professor at McGill University and leads the Mechanics, Actuation, Computation for Robotics group (MACRObotics), where she focuses on creating innovative tools and techniques to enhance robot safety, capabilities and morphological intelligence. Her current research includes automated design and control of soft and stretchable robots, biodegradable material development for soft robots, and simulation. Sedal has a PhD in mechanical engineering from the University of Michigan (2020) and a BSc in mechanical engineering from MIT. Alongside her research, she serves as an associate editor for the IEEE International Conference on Robotics and Automation (ICRA) and the Robotics: Science and Systems conference.

Current Students

Shiyao Ni
Master's Research - McGill University
Website
Github
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Portia Rayner
Master's Research - McGill University

Publications

Acoustic tactile sensing for mobile robot wheels
Wilfred Mason
David Brenken
Falcon Z. Dai
Ricardo Gonzalo Cruz Castillo
Olivier St-Martin Cormier
Audrey Sedal
2024-02-28
ArXiv (preprint)
doi.org
arxiv.org
Lagrangian Properties and Control of Soft Robots Modeled with Discrete Cosserat Rods
Lekan Molu
Shaoru Chen
Audrey Sedal
The characteristic ``in-plane"bending associated with soft robots' deformation make them preferred over rigid robots in sophisticated manipu… (see more)lation and movement tasks. Executing such motion strategies to precision in soft deformable robots and structures is however fraught with modeling and control challenges given their infinite degrees-of-freedom. Imposing \textit{piecewise constant strains} (PCS) across (discretized) Cosserat microsolids on the continuum material however, their dynamics become amenable to tractable mathematical analysis. While this PCS model handles the characteristic difficult-to-model ``in-plane"bending well, its Lagrangian properties are not exploited for control in literature neither is there a rigorous study on the dynamic performance of multisection deformable materials for ``in-plane"bending that guarantees steady-state convergence. In this sentiment, we first establish the PCS model's structural Lagrangian properties. Second, we exploit these for control on various strain goal states. Third, we benchmark our hypotheses against an Octopus-inspired robot arm under different constant tip loads. These induce non-constant ``in-plane"deformation and we regulate strain states throughout the continuum in these configurations. Our numerical results establish convergence to desired equilibrium throughout the continuum in all of our tests. Within the bounds here set, we conjecture that our methods can find wide adoption in the control of cable- and fluid-driven multisection soft robotic arms; and may be extensible to the (learning-based) control of deformable agents employed in simulated, mixed, or augmented reality.
2023-12-10
ArXiv (preprint)
doi.org
arxiv.org