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20172021
most citedA Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization

4 citations · 6 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.RO20212 cited

Lyapunov-stable neural-network control

Hongkai Dai, Benoit Landry, Lujie Yang +2

Deep learning has had a far reaching impact in robotics. Specifically, deep reinforcement learning algorithms have been highly effective in synthesizing neural-network controllers…

cs.RO2021

Vision-based Autonomous Disinfection of High Touch Surfaces in Indoor Environments

Sean Roelofs, Benoit Landry, Myra Kurosu Jalil +4

Autonomous systems have played an important role in response to the Covid-19 pandemic. Notably, there have been multiple attempts to leverage Unmanned Aerial Vehicles (UAVs) to dis…

cs.RO2019

Bilevel Optimization for Planning through Contact: A Semidirect Method

Benoit Landry, Joseph Lorenzetti, Zachary Manchester +1

Many robotics applications, from object manipulation to locomotion, require planning methods that are capable of handling the dynamics of contact. Trajectory optimization has been…

cs.RO20194 cited

A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization

Benoit Landry, Zachary Manchester, Marco Pavone

Many problems in modern robotics can be addressed by modeling them as bilevel optimization problems. In this work, we leverage augmented Lagrangian methods and recent advances in a…

cs.RO2017

Perception-Aware Motion Planning via Multiobjective Search on GPUs

Brian Ichter, Benoit Landry, Edward Schmerling +1

In this paper we describe a framework towards computing well-localized, robust motion plans through the perception-aware motion planning problem, whereby we seek a low-cost motion…