17 citations · 37 across the 9 of their papers we have counts for
11 papers
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…
Eigenvalues as a Metric for Memory Dynamics in Sequence Models
Rahel Rickenbach, Jelena Trisovic, Alexandre Didier +2
While softmax attention drives state-of-the-art performance in sequence modeling, its quadratic complexity motivates linear alternatives such as state space models (SSMs). Structur…
Physics-informed learning under mixing: How physical knowledge speeds up learning
Anna Scampicchio, Leonardo F. Toso, Rahel Rickenbach +2
A major challenge in physics-informed machine learning is to understand how the incorporation of prior domain knowledge affects learning rates when data are dependent. Focusing on…
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…
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.…
Inverse Optimal Control with Constraint Relaxation
Rahel Rickenbach, Amon Lahr, Melanie N. Zeilinger
Inverse optimal control (IOC) is a promising paradigm for learning and mimicking optimal control strategies from capable demonstrators, or gaining a deeper understanding of their i…
Inverse Optimal Control as an Errors-in-Variables Problem
Rahel Rickenbach, Anna Scampicchio, Melanie N. Zeilinger
Inverse optimal control (IOC) is about estimating an unknown objective of interest given its optimal control sequence. However, truly optimal demonstrations are often difficult to…
Time Dependent Inverse Optimal Control using Trigonometric Basis Functions
Rahel Rickenbach, Elena Arcari, Melanie N. Zeilinger
The choice of objective is critical for the performance of an optimal controller. When control requirements vary during operation, e.g. due to changes in the environment with which…
Active Learning-based Model Predictive Coverage Control
Rahel Rickenbach, Johannes Köhler, Anna Scampicchio +2
The problem of coverage control, i.e., of coordinating multiple agents to optimally cover an area, arises in various applications. However, coverage applications face two major cha…
Chronos and CRS: Design of a miniature car-like robot and a software framework for single and multi-agent robotics and control
Andrea Carron, Sabrina Bodmer, Lukas Vogel +6
From both an educational and research point of view, experiments on hardware are a key aspect of robotics and control. In the last decade, many open-source hardware and software fr…