Student Projects
Hardware-in-the-Loop Testing of Cerebrospinal Fluid Shunt Systems for Hydrocephalus Patients
Hydrocephalus is a medical condition characterized by the disturbed dynamics of cerebrospinal fluid (CSF) and its excessive accumulation in the brain ventricles. In contemporary therapy, a shunt system is implanted that drains CSF from the ventricles into the peritoneal space. While various types of shunt systems exist, they are essentially all based on passive mechanical pressure valves that are driven by the external pressure gradient. This limits the efficacy of these shunts and complications such as over- and underdrainage may occur. To improve the therapy of hydrocephalus, we are working towards intelligent mechatronic shunt systems that are capable of monitoring vital signs and adapting CSF drainage according to the patient’s actual needs. In this project, you will support the technical upgrade of an existing hardware-in-the-loop test bench that is used for the evaluation of existing shunt systems and the development of smart shunt system.
Keywords
Estimation and Control, Mechatronic Systems
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Student Assistant / HiWi
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Published since: 2024-12-19 , Earliest start: 2025-01-01
Applications limited to ETH Zurich
Organization Research Zeilinger
Hosts Flürenbrock Fabian
Topics Mathematical Sciences , Engineering and Technology
Stereo Image Tracking for Automated Sheep Pose Estimation and Synchronization with Pressure Data in In-Vivo Trials
This project focuses on developing a stereo vision-based pipeline to track 3D sheep poses over 24 hours, synchronizing the data with body pressure readings during chronic in-vivo trials. Leveraging neural networks, the system will address challenges like occlusions and multi-subject tracking. The goal is to synchronize poses with pressure measurements for insights into normal pressure hydrocephalus.
Keywords
Stereo vision, pose estimation, neural networks, hydrocephalus
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Semester Project
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Published since: 2024-12-17 , Earliest start: 2025-02-01 , Latest end: 2025-11-30
Applications limited to ETH Zurich
Organization Research Zeilinger
Hosts Roncoroni Martina
Topics Information, Computing and Communication Sciences
Digital Twin for Spot's Home
MOTIVATION ⇾ Creating a digital twin of the robot's environment is crucial for several reasons: 1. Simulate Different Robots: Test various robots in a virtual environment, saving time and resources. 2. Accurate Evaluation: Precisely assess robot interactions and performance. 3. Enhanced Flexibility: Easily modify scenarios to develop robust systems. 4. Cost Efficiency: Reduce costs by identifying issues in virtual simulations. 5. Scalability: Replicate multiple environments for comprehensive testing. PROPOSAL We propose to create a digital twin of our Semantic environment, designed in your preferred graphics Platform to be able to simulate Reinforcement Learning agents in the digital environment, to create a unified evaluation platform for robotic tasks.
Keywords
Digital Twin, Robotics
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Semester Project , Master Thesis
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Published since: 2024-12-17 , Earliest start: 2025-01-05
Applications limited to University of Zurich , ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Computer Vision and Geometry Group
Hosts Blum Hermann , Portela Tifanny , Bauer Zuria, Dr. , Trisovic Jelena
Topics Information, Computing and Communication Sciences
Differential Particle Simulation for Robotics
This project focuses on applying differential particle-based simulation to address challenges in simulating real-world robotic tasks involving interactions with fluids, granular materials, and soft objects. Leveraging the differentiability of simulations, the project aims to enhance simulation accuracy with limited real-world data and explore learning robotic control using first-order gradient information.
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Semester Project , Master Thesis
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Published since: 2024-12-09 , Earliest start: 2025-01-01 , Latest end: 2025-12-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Robotic Systems Lab
Hosts Nan Fang , Ma Hao
Topics Engineering and Technology
Conformal Prediction for Distribution Shift Detection in Online Learning
This project investigates the use of conformal prediction for detecting distribution shifts in online learning scenarios, with a focus on robotics applications. Distribution shifts, arising from deviations in task distributions or changes in robot dynamics, pose significant challenges to online learning systems by impacting learning efficiency and model performance. The project aims to develop a robust detection algorithm to address these shifts, classifying task distribution shifts as outliers while dynamically retraining models for characteristic shifts.
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Semester Project , Master Thesis
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Published since: 2024-12-09 , Earliest start: 2025-01-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Ma Hao , Nan Fang
Topics Information, Computing and Communication Sciences , Engineering and Technology
Monitoring and prediction for neuro-intensive care
Delayed cerebral ischemia (DCI) occurs in up to one third of patients with treated aneurysmal subarachnoid hemorrhage. Due to the lack of a reliable biomarker in the clinic, timely detection of DCI is currently highly challenging. In fact, its onset is often missed despite the multimodal monitoring in intensive care, with severe consequences for the patient: Secondary infarctions may lead to severe disability or even death. This project aims at developing a novel bedside measurement system to monitor and predict the risk for DCI in the hospital, filling the current diagnostic gap.
Keywords
Biomedical engineering, clinical data analysis, system modeling, state estimation
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Semester Project , Master Thesis
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Published since: 2024-12-03 , Earliest start: 2025-01-10 , Latest end: 2025-12-19
Organization Research Zeilinger
Hosts Heim Marco
Topics Engineering and Technology
Error bounds for scalable Gaussian process regression
The goal of the project consists in deriving error bounds for the approximate Gaussian process regression method given by the FITC method.
Keywords
Gaussian processes, uncertainty bounds
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Semester Project , Master Thesis
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Published since: 2024-12-02
Organization Research Zeilinger
Hosts Scampicchio Anna
Topics Engineering and Technology
Error bounds for Regularized Trigonometric Regression in the Multi-task setting
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.
Keywords
statistical learning, multi-task learning, uncertainty bounds
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Semester Project , Master Thesis
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Published since: 2024-12-02
Organization Research Zeilinger
Hosts Scampicchio Anna
Topics Engineering and Technology
Learning-based stochastic Model Predictive Control with scalable Gaussian process regression
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.
Keywords
stochastic model predictive control, Gaussian process regression, learning-based control, uncertainty bounds
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Semester Project , Master Thesis
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Published since: 2024-12-02
Organization Research Zeilinger
Hosts Scampicchio Anna
Topics Engineering and Technology
Control-Theoretic Analysis of Deep State Space Models and Transformers
This project explores the control-theoretic foundations of deep state space models (SSMs) and deep attention-based models, focusing on specific properties of their dynamics and training behavior. By bridging insights from control theory and deep learning, the project aims to generate insights that could pave the way for next-generation large language models (LLMs).
Keywords
Control Theory, Deep State Space Models, Mamba, Transformer
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Semester Project
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Published since: 2024-12-02 , Earliest start: 2025-02-09
Organization Research Zeilinger
Hosts Trisovic Jelena , Sieber Jérôme
Topics Mathematical Sciences , Information, Computing and Communication Sciences
Vision-based Autonomous Racing with F1Tenth Car
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.
Keywords
vision-based control, autonomous racing, image processing
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Semester Project
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Published since: 2024-11-29 , Earliest start: 2025-01-15
Organization Research Zeilinger
Hosts Trisovic Jelena
Topics Engineering and Technology
Multi-agent predictive control barrier functions
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.
Keywords
predictive control, multi-agent systems, safety filter, control barrier functions
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Master Thesis
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Published since: 2024-11-12
Applications limited to ETH Zurich
Organization Research Zeilinger
Hosts Didier Alexandre
Topics Information, Computing and Communication Sciences , Engineering and Technology
Online Learning of Dynamic Control for Soft Manipulators
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.
Keywords
online learning, distribution shift, soft robotics, position control
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Master Thesis
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Published since: 2024-11-07 , Earliest start: 2024-11-07 , Latest end: 2025-08-01
Organization Research Zeilinger
Hosts Ma Hao
Topics Engineering and Technology