no code implementations • 4 Mar 2024 • Rudolf Reiter, Katrin Baumgärtner, Rien Quirynen, Moritz Diehl
Nonlinear model predictive control (NMPC) is a popular strategy for solving motion planning problems, including obstacle avoidance constraints, in autonomous driving applications.
no code implementations • 30 Jun 2023 • Yunfan Gao, Florian Messerer, Jonathan Frey, Niels van Duijkeren, Moritz Diehl
This paper presents an implementation of robust model predictive control (MPC) for collision-free reference trajectory tracking for mobile robots.
no code implementations • 3 Apr 2023 • Andrea Ghezzi, Jasper Hoffman, Jonathan Frey, Joschka Boedecker, Moritz Diehl
This work presents a novel loss function for learning nonlinear Model Predictive Control policies via Imitation Learning.
no code implementations • 22 Dec 2022 • Rudolf Reiter, Armin Nurkanović, Jonathan Frey, Moritz Diehl
We specify costs and collision avoidance constraints in the more advantageous coordinate frame, derive appropriate formulations and compare different obstacle constraints.
1 code implementation • 6 Dec 2022 • Shamil Mamedov, Rudolf Reiter, Seyed Mahdi Basiri Azad, Joschka Boedecker, Moritz Diehl, Jan Swevers
The development of fast and safe approximate NMPC holds the potential to accelerate the adoption of flexible robots in industry.
no code implementations • 28 Nov 2022 • Jochem De Schutter, Jakob Harzer, Moritz Diehl
This paper proposes and simulates vertical airborne wind energy (AWE) farms based on multi-aircraft systems with high power density (PD) per ground area.
no code implementations • 23 Oct 2022 • Alessandro Saviolo, Jonathan Frey, Abhishek Rathod, Moritz Diehl, Giuseppe Loianno
Model-based control requires an accurate model of the system dynamics for precisely and safely controlling the robot in complex and dynamic environments.
no code implementations • 4 Apr 2022 • Rudolf Reiter, Florian Messerer, Markus Schratter, Daniel Watzenig, Moritz Diehl
The algorithm therefore learns to predict the observed vehicle trajectory in a least-squares relation to measurement data and to the presumed structure of the predicting NLP.
no code implementations • 20 Nov 2020 • Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl, Frank Hutter
In this work, we present a first-order stochastic algorithm based on a combination of homotopy methods and SGD, called Homotopy-Stochastic Gradient Descent (H-SGD), which finds interesting connections with some proposed heuristics in the literature, e. g. optimization by Gaussian continuation, training by diffusion, mollifying networks.
1 code implementation • 21 Oct 2020 • Barbara Barros Carlos, Tommaso Sartor, Andrea Zanelli, Gianluca Frison, Wolfram Burgard, Moritz Diehl, Giuseppe Oriolo
The advances in computer processor technology have enabled the application of nonlinear model predictive control (NMPC) to agile systems, such as quadrotors.
Robotics Systems and Control Systems and Control Optimization and Control
2 code implementations • 12 Jun 2020 • Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf
We prove a theorem that generalizes the classical duality in the mathematical problem of moments.
1 code implementation • 3 Jun 2020 • Matilde Gargiani, Andrea Zanelli, Moritz Diehl, Frank Hutter
This enables researchers to further study and improve this promising optimization technique and hopefully reconsider stochastic second-order methods as competitive optimization techniques for training DNNs; we also hope that the promise of SGN may lead to forward automatic differentiation being added to Tensorflow or Pytorch.
no code implementations • ICLR 2020 • Matilde Gargiani, Andrea Zanelli, Quoc Tran Dinh, Moritz Diehl, Frank Hutter
Homotopy methods, also known as continuation methods, are a powerful mathematical tool to efficiently solve various problems in numerical analysis, including complex non-convex optimization problems where no or only little prior knowledge regarding the localization of the solutions is available.
no code implementations • 31 Mar 2020 • Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf
In order to anticipate rare and impactful events, we propose to quantify the worst-case risk under distributional ambiguity using a recent development in kernel methods -- the kernel mean embedding.
1 code implementation • 5 Mar 2020 • Gianluca Frison, Moritz Diehl
This paper introduces HPIPM, a high-performance framework for quadratic programming (QP), designed to provide building blocks to efficiently and reliably solve model predictive control problems.
Optimization and Control Systems and Control Systems and Control
1 code implementation • L4DC 2020 • Jia-Jie Zhu, Moritz Diehl, Bernhard Schölkopf
We apply kernel mean embedding methods to sample-based stochastic optimization and control.
1 code implementation • 25 Nov 2019 • Jia-Jie Zhu, Krikamol Muandet, Moritz Diehl, Bernhard Schölkopf
This work presents the concept of kernel mean embedding and kernel probabilistic programming in the context of stochastic systems.
1 code implementation • 30 Oct 2019 • Robin Verschueren, Gianluca Frison, Dimitris Kouzoupis, Niels van Duijkeren, Andrea Zanelli, Branimir Novoselnik, Jonathan Frey, Thivaharan Albin, Rien Quirynen, Moritz Diehl
The acados software package is a collection of solvers for fast embedded optimization, intended for fast embedded applications.
Optimization and Control
1 code implementation • 21 Feb 2019 • Gianluca Frison, Tommaso Sartor, Andrea Zanelli, Moritz Diehl
This BLAS API has lower performance than the BLASFEO API, but it nonetheless outperforms optimized BLAS and especially LAPACK libraries for matrices fitting in cache.
Mathematical Software
2 code implementations • 29 Nov 2017 • Markus Giftthaler, Michael Neunert, Markus Stäuble, Jonas Buchli, Moritz Diehl
This paper introduces a family of iterative algorithms for unconstrained nonlinear optimal control.
Systems and Control Robotics Optimization and Control
6 code implementations • 8 Apr 2017 • Gianluca Frison, Dimitris Kouzoupis, Tommaso Sartor, Andrea Zanelli, Moritz Diehl
BLASFEO is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization.
Mathematical Software