571 citations · 1.3k across the 123 of their papers we have counts for
143 papers
A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment
Angela P. Schoellig, Matthias A. Müller, Sebastian Trimpe +8
Motivated by ten years of Learning-Based Control Invited Sessions at the IEEE Conference on Decision and Control (CDC), this tutorial paper reviews key advances at the intersection…
VIGOR-MPC: Vision-Guided Objective Regulation MPC
Jelena Trisovic, Rahel Rickenbach, Marek Landert +2
Controlling an agent in an environment solely perceived through onboard vision is challenging: perception yields noisy, variable-length, and partially occluded context, while onboa…
MOSAIC: Mode- and Support-Aware Inference-Time Control of Robot Diffusion Models
Sabrina Bodmer, Rahel Rickenbach, René Zurbrügg +2
Generative policies such as diffusion models provide expressive data-driven priors for robot behavior, yet the situations a robot faces at execution time rarely match its training…
Reachability-Guided Sequential Quadratic Programming-Guarded Model Predictive Path Integral for Safe Nonlinear Predictive Control
Alexandre Didier, Jason J. Choi, Namhoon Cho +2
Safe robot control often requires combining long-horizon performance optimization with hard state and input constraints, but existing approaches tend to address this tradeoff parti…
Distributed Model Predictive Control with Connectivity-based Contracts
Jorit Geurts, Danilo Saccani, Melanie N. Zeilinger +1
Teams of mobile robots rely on continuous communication with their neighbors for coordination, yet most distributed model predictive control (DMPC) schemes assume the communication…
Scalable Gaussian Process Regression via Deterministic Trigonometric Features: Uniform Bounds for Safe Model Predictive Control
Julius Jagdt, Johanna Menn, Sebastian Trimpe +2
Learning-based Model Predictive Control (MPC) using Gaussian processes (GPs) is an effective approach for safe control in the presence of model mismatch. High-probability safety gu…
Anytime Plug-and-Play Control with Contract-Based Distributed MPC
Sabrina Bodmer, Danilo Saccani, Melanie N. Zeilinger +1
A central challenge in many mobile multi-robot applications is that communication topologies are inherently time-varying. Agents may enter or exit the network and such changes cann…
Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Control
Sabrina Bodmer, René Zurbrügg, Tifanny Portela +5
Diffusion models sample effectively from high-dimensional, multimodal distributions, but their outputs may violate deployment constraints. For task-space robot policies, generated…
Sampling-Based Safe Reinforcement Learning
Luca Vignola, Bruce D. Lee, Manish Prajapat +4
Safe exploration remains a fundamental challenge in reinforcement learning (RL), limiting the deployment of RL agents in the real world. We propose Sampling-Based Safe Reinforcemen…
Robust Adaptive Predictive Control for Hook-Based Aerial Transportation Between Moving Platforms
Péter Antal, Andrea Carron, Melanie Zeilinger +2
This paper presents a novel model predictive control (MPC) approach for autonomous pick-and-place between moving platforms with a hook-equipped aerial manipulator. First, for accur…
VISION-SLS: Safe Perception-Based Control from Learned Visual Representations via System Level Synthesis
Antoine P. Leeman, Shuyu Zhan, Melanie N. Zeilinger +1
We propose VISION-SLS, a method for nonlinear output-feedback control from high-resolution RGB images which provides robust constraint satisfaction guarantees under calibrated unce…
Goal-oriented safe active learning for predictive control using Bayesian recurrent neural networks
Laura Boca de Giuli, Alessio La Bella, Manish Prajapat +4
A key challenge in learning-based model predictive control (MPC) is to collect informative data online for model adaptation while ensuring safety and without penalising control per…
Efficient Uniform Feasible-Set Sampling for Approximate Linear MPC
Elias Milios, Felix Berkel, Felix Gruber +2
Model Predictive Control (MPC) offers safe and near-optimal control but suffers from high computational costs. Approximate MPC (AMPC) mitigates this by learning a cheaper surrogate…
Bridging RL and MPC for mixed-integer optimal control with application to Formula 1 race strategies
Joschua Wüthrich, Romir Damle, Giona Fieni +3
We propose a hybrid reinforcement learning (RL) and model predictive control (MPC) framework for mixed-integer optimal control, where discrete variables enter the cost and dynamics…
Distributed Predictive Control Barrier Functions: Towards Scalable Safety Certification in Modular Multi-Agent Systems
Jonas Ohnemus, Alexandre Didier, Ahmed Aboudonia +2
We consider safety-critical multi-agent systems with distributed control architectures and potentially varying network topologies. While learning-based distributed control enables…
Real-Time Online Learning for Model Predictive Control using a Spatio-Temporal Gaussian Process Approximation
Lars Bartels, Amon Lahr, Andrea Carron +1
Learning-based model predictive control (MPC) can enhance control performance by correcting for model inaccuracies, enabling more precise state trajectory predictions than traditio…
Optimistic Online LQR via Intrinsic Rewards
Marcell Bartos, Bruce D. Lee, Lenart Treven +3
Optimism in the face of uncertainty is a popular approach to balance exploration and exploitation in reinforcement learning. Here, we consider the online linear quadratic regulator…
Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
Amon Lahr, Anna Scampicchio, Johannes Köhler +1
Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning-based control. I…
SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference
Hao Ma, Melis Ilayda Bal, Liang Zhang +4
Modern large language models are increasingly deployed under compute and memory constraints, making flexible control of model capacity a central challenge. While sparse and low-ran…
Learning-based Approximate Model Predictive Control for an Impact Wrench Tool
Mark Benazet, Francesco Ricca, Dario Bralla +2
Learning-based model predictive control has emerged as a powerful approach for handling complex dynamics in mechatronic systems, enabling data-driven performance improvements while…
Statistically Consistent Approximate Model Predictive Control
Elias Milios, Kim P. Wabersich, Felix Berkel +2
Model Predictive Control (MPC) offers rigorous safety and performance guarantees but is computationally intensive. Approximate MPC (AMPC) aims to circumvent this drawback by learni…
Unifying Sequential Quadratic Programming and Linear-Parameter-Varying Algorithms for Real-Time Model Predictive Control
Kristóf Floch, Amon Lahr, Roland Tóth +1
This paper presents a unified framework that connects sequential quadratic programming (SQP) and the iterative linear-parameter-varying model predictive control (LPV-MPC) technique…
Stability of Certainty-Equivalent Adaptive LQR for Linear Systems with Unknown Time-Varying Parameters
Marcell Bartos, Johannes Köhler, Florian Dörfler +1
Standard model-based control design deteriorates when the system dynamics change during operation. To overcome this challenge, online and adaptive methods have been proposed in the…
Robust reduced-order model predictive control using peak-to-peak analysis of filtered signals
Johannes Köhler, Carlo Scholz, Melanie Zeilinger
We address the design of a model predictive control (MPC) scheme for large-scale linear systems using reduced-order models (ROMs). Our approach uses a ROM, leverages tools from rob…