20 citations · 49 across the 19 of their papers we have counts for
20 papers
Active learning-based Bayesian optimization in the realm of copper slag-blended cement systems
Debadri Som, Rayna Maheshwari, Mathijs Schuurmans +2
Accelerated mix design optimization is critical for deploying low-carbon supplementary cementitious materials (SCMs) because traditional experimental approaches require extensive t…
Probabilistic Safety under Arbitrary Disturbance Distributions using Piecewise-Affine Control Barrier Functions
Matisse Teuwen, Mathijs Schuurmans, Panagiotis Patrinos
We propose a simple safety filter design for stochastic discrete-time systems based on piecewise affine probabilistic control barrier functions, providing an appealing balance betw…
Risk-Sensitive Model Predictive Control for Interaction-Aware Planning -- A Sequential Convexification Algorithm
Renzi Wang, Mathijs Schuurmans, Panagiotis Patrinos
This paper considers risk-sensitive model predictive control for stochastic systems with a decision-dependent distribution. This class of systems is commonly found in human-robot i…
EM++: A parameter learning framework for stochastic switching systems
Renzi Wang, Alexander Bodard, Mathijs Schuurmans +1
This paper proposes a general switching dynamical system model, and a custom majorization-minimization-based algorithm EM++ for identifying its parameters. For certain families of…
Optimal Intraday Power Trading for Single-Price Balancing Markets: An Adaptive Risk-Averse Strategy using Mixture Models
Robin Bruneel, Mathijs Schuurmans, Panagiotis Patrinos
Efficient markets are characterised by profit-driven participants continuously refining their positions towards the latest insights. Margins for profit generation are generally sma…
Distributionally Robust Optimization using Cost-Aware Ambiguity Sets
Mathijs Schuurmans, Panagiotis Patrinos
We present a novel framework for distributionally robust optimization (DRO), called cost-aware DRO (CADRO). The key idea of CADRO is to exploit the cost structure in the design of…
SPOCK: A proximal method for multistage risk-averse optimal control problems
Alexander Bodard, Ruairi Moran, Mathijs Schuurmans +2
Risk-averse optimal control problems have gained a lot of attention in the last decade, mostly due to their attractive mathematical properties and practical importance. They can be…
Interaction-aware Model Predictive Control for Autonomous Driving
Renzi Wang, Mathijs Schuurmans, Panagiotis Patrinos
Lane changing and lane merging remains a challenging task for autonomous driving, due to the strong interaction between the controlled vehicle and the uncertain behavior of the sur…
Safe, Learning-Based MPC for Highway Driving under Lane-Change Uncertainty: A Distributionally Robust Approach
Mathijs Schuurmans, Alexander Katriniok, Christopher Meissen +2
We present a case study applying learning-based distributionally robust model predictive control to highway motion planning under stochastic uncertainty of the lane change behavior…
Alpaqa: A matrix-free solver for nonlinear MPC and large-scale nonconvex optimization
Pieter Pas, Mathijs Schuurmans, Panagiotis Patrinos
This paper presents alpaqa, an open-source C++ implementation of an augmented Lagrangian method for nonconvex constrained numerical optimization, using the first-order PANOC algori…
Learning MPC for Interaction-Aware Autonomous Driving: A Game-Theoretic Approach
Brecht Evens, Mathijs Schuurmans, Panagiotis Patrinos
We consider the problem of interaction-aware motion planning for automated vehicles in general traffic situations. We model the interaction between the controlled vehicle and surro…
A General Framework for Learning-Based Distributionally Robust MPC of Markov Jump Systems
Mathijs Schuurmans, Panagiotis Patrinos
We present a learning model predictive control (MPC) scheme for chance-constrained Markov jump systems with unknown switching probabilities. Using samples of the underlying Markov…
Data-driven distributionally robust control of partially observable jump linear systems
Mathijs Schuurmans, Panagiotis Patrinos
We study safe, data-driven control of (Markov) jump linear systems with unknown transition probabilities, where both the discrete mode and the continuous state are to be inferred f…
Learning-Based Distributionally Robust Model Predictive Control of Markovian Switching Systems with Guaranteed Stability and Recursive Feasibility
Mathijs Schuurmans, Panagiotis Patrinos
We present a data-driven model predictive control scheme for chance-constrained Markovian switching systems with unknown switching probabilities. Using samples of the underlying Ma…
Learning-Based Risk-Averse Model Predictive Control for Adaptive Cruise Control with Stochastic Driver Models
Mathijs Schuurmans, Alexander Katriniok, Hongtei Eric Tseng +1
We propose a learning-based, distributionally robust model predictive control approach towards the design of adaptive cruise control (ACC) systems. We model the preceding vehicle a…
Data-driven distributionally robust LQR with multiplicative noise
Peter Coppens, Mathijs Schuurmans, Panagiotis Patrinos
We present a data-driven method for solving the linear quadratic regulator problem for systems with multiplicative disturbances, the distribution of which is only known through sam…
Risk-averse risk-constrained optimal control
Pantelis Sopasakis, Mathijs Schuurmans, Panagiotis Patrinos
Multistage risk-averse optimal control problems with nested conditional risk mappings are gaining popularity in various application domains. Risk-averse formulations interpolate be…
Nonlinear Model Predictive Control for Distributed Motion Planning in Road Intersections Using PANOC
Alexander Katriniok, Pantelis Sopasakis, Mathijs Schuurmans +1
The coordination of highly automated vehicles (or agents) in road intersections is an inherently nonconvex and challenging problem. In this paper, we propose a distributed motion p…
Safe Learning-Based Control of Stochastic Jump Linear Systems: a Distributionally Robust Approach
Mathijs Schuurmans, Pantelis Sopasakis, Panagiotis Patrinos
We consider the problem of designing control laws for stochastic jump linear systems where the disturbances are drawn randomly from a finite sample space according to an unknown di…
Efficient semantic image segmentation with superpixel pooling
Mathijs Schuurmans, Maxim Berman, Matthew B. Blaschko
In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation. Superpixel pooling is a flexible and efficient replacement f…