Publications

Relative Almost Sure Regret Bounds for Certainty Equivalence Control of Markov Jump Systems

Borna Sayedana

Mohammad Afshari

Peter E. Caines

In this paper, we consider learning and control problem in an unknown Markov jump linear system (MJLS) with perfect state observations. We f… (voir plus)irst establish a generic upper bound on regret for any learning based algorithm. We then propose a certainty equivalence-based learning alagrithm and show that this algorithm achieves a regret of

2023-12-13

2023 62nd IEEE Conference on Decision and Control (CDC) (publié)

doi.org

Weighted-Norm Bounds on Model Approximation in MDPs with Unbounded Per-Step Cost

Berk Bozkurt

Aditya Mahajan

Ashutosh Nayyar

Yi Ouyang

We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov Decision Process (MDP) …

2023-12-13

IEEE Conference on Decision and Control (publié)

doi.org

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

Alexandre AGM Duval

Simon V. Mathis

Chaitanya K. Joshi

Victor Schmidt

Santiago Miret

Fragkiskos D. Malliaros

Taco Cohen

Pietro Lio’

Yoshua Bengio

Michael M. Bronstein

2023-12-12

ArXiv (prépublication)

doi.org

arxiv.org

Efficient Graphics Representation with Differentiable Indirection

Sayantan Datta

Carl Marshall

Zhao Dong

Zhengqin Li

Derek Nowrouzezahrai

We introduce differentiable indirection – a novel learned primitive that employs differentiable multi-scale lookup tables as an effective … (voir plus)substitute for traditional compute and data operations across the graphics pipeline. We demonstrate its flexibility on a number of graphics tasks, i.e., geometric and image representation, texture mapping, shading, and radiance field representation. In all cases, differentiable indirection seamlessly integrates into existing architectures, trains rapidly, and yields both versatile and efficient results.

2023-12-11

SIGGRAPH Asia 2023 Conference Papers (publié)

doi.org

arxiv.org

Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models

Arman Maesumi

Paul Guerrero

Noam Aigerman

Vladimir Kim

Matthew Fisher

Siddhartha Chaudhuri

Daniel Ritchie

2023-12-11

SIGGRAPH Asia 2023 Conference Papers (publié)

doi.org

arxiv.org

Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks

MohammadReza Davari

Eugene Belilovsky

2023-12-11

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.

2023-12-10

ArXiv (prépublication)

doi.org

arxiv.org

Maximum flow-based formulation for the optimal location of electric vehicle charging stations

Pierre-Luc Parent

Margarida Carvalho

Miguel F. Anjos

Ribal Atallah

With the increasing effects of climate change, the urgency to step away from fossil fuels is greater than ever before. Electric vehicles (EV… (voir plus)s) are one way to diminish these effects, but their widespread adoption is often limited by the insufficient availability of charging stations. In this work, our goal is to expand the infrastructure of EV charging stations, in order to provide a better quality of service in terms of user satisfaction (and availability of charging stations). Specifically, our focus is directed towards urban areas. We first propose a model for the assignment of EV charging demand to stations, framing it as a maximum flow problem. This model is the basis for the evaluation of user satisfaction with a given charging infrastructure. Secondly, we incorporate the maximum flow model into a mixed‐integer linear program, where decisions on the opening of new stations and on the expansion of their capacity through additional outlets is accounted for. We showcase our methodology for the city of Montreal, demonstrating the scalability of our approach to handle real‐world scenarios. We conclude that considering both spacial and temporal variations in charging demand is meaningful when solving realistic instances.

2023-12-10

ArXiv (prépublication)

doi.org

arxiv.org

A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings

Vincent Taboga

Hanane Dagdougui

The surge in electricity use, coupled with the dependency on intermittent renewable energy sources, poses significant hurdles to effectively… (voir plus) managing power grids, particularly during times of peak demand. Demand Response programs and energy conservation measures are essential to operate energy grids while ensuring a responsible use of our resources This research combines distributed optimization using ADMM with Deep Learning models to plan indoor temperature setpoints effectively. A two-layer hierarchical structure is used, with a central building coordinator at the upper layer and local controllers at the thermal zone layer. The coordinator must limit the building's maximum power by translating the building's total power to local power targets for each zone. Local controllers can modify the temperature setpoints to meet the local power targets. The resulting control algorithm, called Distributed Planning Networks, is designed to be both adaptable and scalable to many types of buildings, tackling two of the main challenges in the development of such systems. The proposed approach is tested on an 18-zone building modeled in EnergyPlus. The algorithm successfully manages Demand Response peak events.

2023-12-08

ArXiv (prépublication)

doi.org

arxiv.org

Filtering Pixel Latent Variables for Unmixing Noisy and Undersampled Volumetric Images

Catherine Bouchard

Andréanne Deschênes

Vincent Boulanger

Jean-Michel Bellavance

Flavie Lavoie-Cardinal

Christian Gagné

2023-12-08