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20242026
most citedA model predictive control framework with robust stability guarantees under unbounded disturbances

2 citations · 3 across the 26 of their papers we have counts for

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11 papers · 1 filter

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

An MPC framework for efficient navigation of mobile robots in cluttered environments

Johannes Köhler, Daniel Zhang, Raffaele Soloperto +2

We present a model predictive control (MPC) framework for efficient navigation of mobile robots in cluttered environments. The proposed approach integrates a finite-segment shortes…

cs.RO2025

ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning

Rahel Rickenbach, Alan A. Lahoud, Erik Schaffernicht +2

The computational burden of model predictive control (MPC) limits its application on real-time systems, such as robots, and often requires the use of short prediction horizons. Thi…

cs.RO2025

DEMONSTRATE: Zero-shot Language to Robotic Control via Multi-task Demonstration Learning

Rahel Rickenbach, Bruce Lee, René Zurbrügg +2

The integration of large language models (LLMs) with control systems has demonstrated significant potential in various settings, such as task completion with a robotic manipulator.…