Research Zeilinger

Open Opportunities

Error bounds for scalable Gaussian process regression ETH Zurich Research Zeilinger The goal of the project consists in deriving error bounds for the approximate Gaussian process regression method given by the FITC method. Engineering and Technology Master Thesis, Semester Project

Error bounds for Regularized Trigonometric Regression in the Multi-task setting ETH Zurich Research Zeilinger Multi-task learning is the problem of jointly learning multiple functions that are “related” to each other. By leveraging this similarity, estimation performance can be improved on each (possibly unseen) task, and one can make an efficient use of the available data. The project aims at deriving uncertainty bounds around the multi-task-system estimates. Specifically, the candidate will work with the regularized trigonometric regression inspired by the so-called sparse-spectrum Gaussian process regression, investigate the issue of bias learning (i.e., finding the features that encode similarity among tasks) and derive error bounds for it, possibly setting the analysis in the statistical learning framework. Engineering and Technology Master Thesis, Semester Project

Learning-based stochastic Model Predictive Control with scalable Gaussian process regression ETH Zurich Research Zeilinger One of the key ingredients in Model Predictive Control (MPC) schemes is an effective model of the dynamical system’s response to external inputs. However, first- principles models are often not accurate enough, as there might be unknown external disturbances and model mismatches. To address this, learning-based control aims at complementing nominal models with data-based ones, which can be refined online as new system observations are gathered. Thus, such a model should be both expressive and fast to update. This project focuses on a learning-based stochastic MPC scheme, where uncertainty in the model is learned with an approximate Gaussian process, namely the regularized trigonometric regression stemming from the so- called sparse-spectrum Gaussian processes. To this aim, the candidate will review the available uncertainty bounds around these approximate Gaussian-process-based estimates and incorporate them in the MPC formulation. The chance-constraints thereby obtained are then to be analyzed to rigorously prove recursive feasibility and stability of the closed-loop system. Engineering and Technology Master Thesis, Semester Project

Vision-based Autonomous Racing with F1Tenth Car ETH Zurich Research Zeilinger In this semester thesis, our goal is to enable an F1Tenth car, an autonomous vehicle at 1:10 scale of a Formula 1 car, to race safely on a track that is perceived through RGB-D images captured by an onboard camera. Engineering and Technology Semester Project

Multi-agent predictive control barrier functions ETH Zurich Research Zeilinger In this project, we want to explore possible extensions of predictive control barrier functions to the multi-agent setting. Predictive control barrier functions [1] allow certifying safety of a system in terms of constraint satisfaction and provide stability guarantees with respect to the set of safe states in case of initial feasibility. This allows augmenting any human or learning-based controller with closed-loop guarantees through a so-called safety filter [2] which is agnostic to the primary control objective. As current formulations are restricted to single agents, the goal is to investigate how this formulation can be extended for multi-agent applications and how the interactions between the agents can be exploited in order to reduce computational overhead. Engineering and Technology, Systems Theory and Control Master Thesis

Online Learning of Dynamic Control for Soft Manipulators ETH Zurich Research Zeilinger This project aims to develop an online learning framework for achieving precise position control of a soft robotic arm while adapting to time-varying system dynamics. Engineering and Technology Master Thesis