222 citations · 250 across the 11 of their papers we have counts for
12 papers
End-to-end QP-based policies: A unified perspective on robust control and robot learning
Fausto Vega, Priyanka Supraja Balaji, Chase Dunaway +3
We present an end-to-end QP-based policy framework that enables systematic policy construction with minimal domain-specific design, while preserving the transparency and interpreta…
Fast and Robust Temporal Logic Planning via ADMM-based Trajectory Optimization
Lukas Pries, Joris Verhagen, Jon Arrizabalaga +3
We present a fast numerical method for safe continuous-time motion planning under Temporal Logic (TL) specifications. The method generates smooth continuous trajectories that remai…
ADMM-based Continuous Trajectory Optimization in Graphs of Convex Sets
Lukas Pries, Jon Arrizabalaga, Zachary Manchester +1
This paper presents a numerical solver for computing continuous trajectories in non-convex environments. Our approach relies on a customized implementation of the Alternating Direc…
PHODCOS: Pythagorean Hodograph-based Differentiable Coordinate System
Jon Arrizabalaga, Fausto Vega, Zbyněk ŠÍR +2
This paper presents PHODCOS, an algorithm that assigns a moving coordinate system to a given curve. The parametric functions underlying the coordinate system, i.e., the path functi…
A Universal Formulation for Path-Parametric Planning and Control
Jon Arrizabalaga, Zbyněk ŠÍR, Zachary Manchester +1
We present a unified framework for path-parametric planning and control. This formulation is universal as it standardizes the entire spectrum of path-parametric techniques -- from…
Differentiable Collision-Free Parametric Corridors
Jon Arrizabalaga, Zachary Manchester, Markus Ryll
This paper presents a method to compute differentiable collision-free parametric corridors. In contrast to existing solutions that decompose the obstacle-free space into multiple c…
Geometric Slosh-Free Tracking for Robotic Manipulators
Jon Arrizabalaga, Lukas Pries, Riddhiman Laha +3
This work focuses on the agile transportation of liquids with robotic manipulators. In contrast to existing methods that are either computationally heavy, system/container specific…
Learning for CasADi: Data-driven Models in Numerical Optimization
Tim Salzmann, Jon Arrizabalaga, Joel Andersson +2
While real-world problems are often challenging to analyze analytically, deep learning excels in modeling complex processes from data. Existing optimization frameworks like CasADi…
Pose-Following with Dual Quaternions
Jon Arrizabalaga, Markus Ryll
This work focuses on pose-following, a variant of path-following in which the goal is to steer the system's position and attitude along a path with a moving frame attached to it. F…
SCTOMP: Spatially Constrained Time-Optimal Motion Planning
Jon Arrizabalaga, Markus Ryll
This paper focuses on spatial time-optimal motion planning, a generalization of the exact time-optimal path following problem that allows the system to plan within a predefined spa…
Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms
Tim Salzmann, Elia Kaufmann, Jon Arrizabalaga +3
Model Predictive Control (MPC) has become a popular framework in embedded control for high-performance autonomous systems. However, to achieve good control performance using MPC, a…
Towards Time-Optimal Tunnel-Following for Quadrotors
Jon Arrizabalaga, Markus Ryll
Minimum-time navigation within constrained and dynamic environments is of special relevance in robotics. Seeking time-optimality, while guaranteeing the integrity of time-varying s…