Portrait de Audrey Sedal

Audrey Sedal

Membre académique associé
Professeure adjointe, McGill University, Département de l'ingénierie médicale
Sujets de recherche
Apprentissage par renforcement
Modèles génératifs
Optimisation
Site web
Google Scholar
LinkedIn

Biographie

Audrey Sedal, professeure adjointe au Département de génie mécanique de l'Université McGill, dirige le groupe MACRObotics (Mechanics, Actuation, Computation for Robotics), où elle se concentre sur la création de techniques et d'outils innovants pour améliorer la sécurité, les capacités et l'intelligence morphologique des robots. Ses recherches actuelles portent notamment sur la conception automatisée et le contrôle de robots souples et extensibles, le développement de matériaux biodégradables pour les robots souples, ainsi que la simulation. La professeure Sedal a obtenu un doctorat en génie mécanique de l'Université du Michigan en 2020. Elle détient également un diplôme de premier cycle en génie mécanique du Massachusetts Institute of Technology (MIT). En plus de ses recherches, Audrey Sedal agit en tant qu'éditrice associée pour l’International Conference on Robotics and Automation (IEEE ICRA) et la conférence Robotics: Science and Systems.

Étudiants actuels

Frances McBride
Doctorat - McGill
Shiyao Ni
Maîtrise recherche - McGill
Site web
Github
Google Scholar

Publications

Evaluation of data-driven kinematic models for autonomous control of continuum robotic in-situ bioprinters
Swen A.T. Groen
David Brenken
Samuel Smocot
James Richard Forbes
Luc Mongeau
Audrey Sedal
Minimally invasive in-situ bioprinting involves the direct deposition of hydrogels within the body to reconstruct tissue defects. These biop… (voir plus)rinters use soft robotic printheads to extrude hydrogels through a hollow channel and nozzle. The accurate control of the nozzle tip position is critical for safety and shape fidelity. Due to a lack of sensing integration, existing control strategies are limited to feedforward models and are design-specific, thereby increasing development cost and complexity. This present study systematically compared three different data-driven modeling strategies for autonomous control of a cable-driven continuum in-situ bioprinter: 1) Polynomial regression, 2) Gaussian process regression, and 3) a neural network. Submillimeter accuracy was achieved for both the Gaussian process and the neural network in static measurements. The Polynomial regression model had a 1.67 mm accuracy. Dynamic trajectory tracking indicated that the performance of the neural network was comparable to that of the polynomial regression model and lower than that of the Gaussian process. Printing of different shaped constructs yielded minor visual deviations across all models from the target shapes. These results support the feasibility of real-time autonomous control for minimally invasive in-situ bioprinters and indicate the advantages of the different models during the design process of novel printing strategies.
2026-04-06
IEEE International Conference on Soft Robotics (publié)
doi.org
Observability-Informed Optimal Sensor Placement for Soft Robots
Samuel Smocot
James R. Forbes
Audrey Sedal
This paper presents the application and experimental evaluation of a systematic method for optimal sensor placement in soft robots. Existing… (voir plus) methods either lack generalizability across different soft robot morphologies or do not account for system dynamics. The applied method uses convex optimization to find the optimal sensor configuration that maximizes an observability Gramian-based metric. The framework is experimentally evaluated using position and strain measurements on a soft continuum arm. Kalman filter state estimates using optimal sensor placements yield lower reconstruction error than a baseline across all sinusoidal input trials, with improvements on the order of millimeters. This case study shows that linear control theory tools can guide optimal sensor placement in soft robots, suggesting an interpretable approach to sensor placement that may extend to other morphologies.
2026-04-06
IEEE International Conference on Soft Robotics (publié)
doi.org
Programmable Membrane Shape via Localized Stiffening of a Granular Suspension
Omar Khater
Karim Saliba
Audrey Sedal
Granular actuators are often implemented with as many vacuum pumps as jamming segments. For highly articulated motion, these segments increa… (voir plus)se mechanical design complexity. In this work, we propose cooperative use of pumps in a monolithic liquid-granular suspension to locally control jamming behavior. As an example of a granular soft robot, we change the shape of a buckled membrane by varying the local concentration of a particle-water suspension. We then develop and validate a numerical model based on localized stiffening to explain the underlying mechanisms behind membrane motion. We finally demonstrate that this scheme enables actuation programmed by pump sequences. This novel actuation may further enable localized jamming in arbitrary internal structures, ultimately leading to simple soft robots with programmable articulated motion.
2026-04-06
IEEE International Conference on Soft Robotics (publié)
doi.org
They Hear Me Rolling: Design and Characterization of a Distributed, Rolling Acoustic-Tactile Sensor
Wilfred Mason
David Brenken
Olivier St-Martin Cormier
Audrey Sedal
Tactile sensor design has been widely explored at the centimeter-scale; fewer explorations exist in larger scale systems with varied geometr… (voir plus)ies. We present a meter-scale tactile sensor for wheeled robotic platforms based on a flexible acoustic waveguide. This sensor architecture performs contact sensing over the surface of a rotating wheel with a single transducer that is separated from the sensing surface. The design and characterization of the sensor are presented, along with a demonstration of a state-estimation framework using tactile sensor feedback to measure surface features.
2025-09-30
IEEE Sensors Letters (publié)
doi.org
Acoustic tactile sensing for mobile robot wheels
Wilfred Mason
David Brenken
Falcon Z. Dai
Ricardo Gonzalo Cruz Castillo
Olivier St-Martin Cormier
Audrey Sedal
2024-02-27
ArXiv (prépublication)
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… (voir plus)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.
2023-12-09
ArXiv (prépublication)
doi.org
arxiv.org