activity
20192022
most citedGuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming

119 citations · 145 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2022

Real-Time Model Predictive Control for Industrial Manipulators with Singularity-Tolerant Hierarchical Task Control

Jaemin Lee, Mingyo Seo, Andrew Bylard +2

This paper proposes a real-time model predictive control (MPC) scheme to execute multiple tasks using robots over a finite-time horizon. In industrial robotic applications, we must…

cs.RO2021

ReachBot: A Small Robot for Large Mobile Manipulation Tasks

Stephanie Schneider, Andrew Bylard, Tony G. Chen +3

Robots are widely deployed in space environments because of their versatility and robustness. However, adverse gravity conditions and challenging terrain geometry expose the limita…

cs.RO2021

Composable Geometric Motion Policies using Multi-Task Pullback Bundle Dynamical Systems

Andrew Bylard, Riccardo Bonalli, Marco Pavone

Despite decades of work in fast reactive planning and control, challenges remain in developing reactive motion policies on non-Euclidean manifolds and enforcing constraints while a…

math.OC201926 cited

Trajectory Optimization on Manifolds: A Theoretically-Guaranteed Embedded Sequential Convex Programming Approach

Riccardo Bonalli, Andrew Bylard, Abhishek Cauligi +2

Sequential Convex Programming (SCP) has recently gained popularity as a tool for trajectory optimization due to its sound theoretical properties and practical performance. Yet, mos…

math.OC2019119 cited

GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming

Riccardo Bonalli, Abhishek Cauligi, Andrew Bylard +1

Sequential Convex Programming (SCP) has recently seen a surge of interest as a tool for trajectory optimization. However, most available methods lack rigorous performance guarantee…