activity
20092022
most citedGuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming

119 citations · 355 across the 60 of their papers we have counts for

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Showing 2019 · cs.ROShow all

6 papers · 2 filters

cs.RO2019

Map-Predictive Motion Planning in Unknown Environments

Amine Elhafsi, Boris Ivanovic, Lucas Janson +1

Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods…

cs.RO2019

Efficient Large-Scale Multi-Drone Delivery Using Transit Networks

Shushman Choudhury, Kiril Solovey, Mykel J. Kochenderfer +1

We consider the problem of controlling a large fleet of drones to deliver packages simultaneously across broad urban areas. To conserve energy, drones hop between public transit ve…

cs.RO2019

Revisiting the Asymptotic Optimality of RRT

Kiril Solovey, Lucas Janson, Edward Schmerling +2

RRT* is one of the most widely used sampling-based algorithms for asymptotically-optimal motion planning. This algorithm laid the foundations for optimality in motion planning as a…

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.RO2019

Network Offloading Policies for Cloud Robotics: a Learning-based Approach

Sandeep Chinchali, Apoorva Sharma, James Harrison +6

Today's robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and obje…

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…