Semester Project

Semester Projects at IRIS

Mid-Range Path Planning Integrating Vision with LLM and Depth Sensing

Robotic Systems Lab

This project aims to advance the field of robotic navigation by focusing on mid-range path planning, a crucial layer that connects the overarching routes designed by global planning and the immediate, reactive maneuvers of local planning. The project will develop a neural network model capable of generating a sequence of waypoints toward a specified 3D goal position by leveraging current RGB images, GPT cost reasoning from large language model (LLM) , and estimated depth images. This integration will facilitate more efficient navigation through complex environments by smoothing transitions between planning layers and optimizing route adjustments in real-time.

Keywords

Robot Planning; Vision Learning; LLM (Vision GPT) Reason,

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2024-04-26 , Earliest start: 2024-05-01 , Latest end: 2025-01-01

Organization Robotic Systems Lab

Hosts Yang Fan

Topics Information, Computing and Communication Sciences

Inverse Reinforcement Learning from Expert Pilots

Robotics and Perception

Use Inverse Reinforcement Learning (IRL) to learn reward functions from previous expert drone demonstrations.

Keywords

Inverse Reinforcement Learning, Drones, Robotics

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Semester Project , Master Thesis

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Published since: 2024-04-25 , Earliest start: 2024-05-15 , Latest end: 2025-01-31

Organization Robotics and Perception

Hosts Geles Ismail

Topics Information, Computing and Communication Sciences , Engineering and Technology

Language-guided Drone Control

Robotics and Perception

Explore the use of large vision language models to control a drone.

Keywords

Human-drone interaction, large language models (LLMs), Robotics

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Semester Project , Master Thesis

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Published since: 2024-04-25 , Earliest start: 2024-05-15 , Latest end: 2025-01-31

Organization Robotics and Perception

Hosts Geles Ismail

Topics Information, Computing and Communication Sciences , Engineering and Technology

Reinforcement Learning for Drone Maneuvers from Human Preferences

Robotics and Perception

Learn complex drone maneuvers from human feedback using Reinforcement Learning (RL).

Keywords

Reinforcement Learning from human feedback (RLHF), Drones, Robotics

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Semester Project , Master Thesis

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Published since: 2024-04-25 , Earliest start: 2024-05-15 , Latest end: 2025-01-31

Organization Robotics and Perception

Hosts Geles Ismail

Topics Information, Computing and Communication Sciences , Engineering and Technology

Supporter State Estimation System

Autonomous Systems Lab

Implementation of a localization and state estimation system for a special ground robot using IMU and 2D LiDAR. The ground robot is part of a Gravity offload system for testing of spacecraft solar arrays (which are designed for microgravity) on earth.

Keywords

State Estimation Localization

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Published since: 2024-04-25

Applications limited to ETH Zurich

Organization Autonomous Systems Lab

Hosts Ott Lionel , Pantic Michael

Topics Information, Computing and Communication Sciences

Master Thesis / Internship: Automated Time Series Analysis in Urinary Tract Assessment in Spinal Cord Injury

Spinal Cord Injury & Artificial Intelligence Lab

The primary objective of this project is to develop an automated pipeline for the identification and recognition of patterns within urodynamic recordings, utilizing urodynamic recording data in conjunction with annotated patterns provided by experts. This endeavor seeks to reduce the susceptibility of interpreting urodynamic recordings to potential errors arising from human judgment and inaccuracies, thereby improving the management of urinary tract complications in patients with spinal cord injury. By implementing a systematic approach to pattern recognition in Bladder Valomue/Pressure Time Series Measurements of urodynamic data, the potential for error in decision-making can be significantly reduced.

Keywords

Spinal Cord Injury, Machine Learning, Deep Learning, Pattern Recognition, Feature Engineering, Time Series Analysis, Signal Processing

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Semester Project , Internship , Master Thesis

Description

**Background** Spinal cord injury (SCI) is a severe medical condition that affects millions of people worldwide. These patients often suffer from other health problems and comorbidities such as urinary tract complications which should be monitored so that the risk is minimized. A urodynamic test is a diagnostic tool used to evaluate the function of the bladder and urethra. The test measures flow and pressure in the urinary tract and can help determine the underlying cause of urinary dysfunction. The major complication of neurogenic lower urinary tract dysfunction is a risk to the upper tracts, so baseline urodynamic studies are performed in patients with SCI to assess symptoms and risk to the upper tracts, so that management can be planned accordingly. The interpretation of urodynamic measurements and the identification of patterns in the resulting graphs are critical components of the decision-making process regarding the administration of the test. However, this process is subject to a significant degree of error due to the involvement of human judgment. There is a pressing need for the development of an automated, systematic approach to pattern recognition in urodynamic data that would reduce the potential for error in decision-making. **Your Tasks** - Retrieve the urodynamic recordings data in conjunction with annotations and patterns provided by experts. - Conduct an extensive review of the current literature on pattern recognition in time series data. - Perform pre-processing procedures on the time-series recordings to eliminate artifacts and enhance data quality. - Develop and implement pipelines utilizing deep learning or feature extraction techniques for accurate pattern recognition in time series data (employing supervised pattern recognition methods). - Fine-tune the developed pipeline to ensure its user-friendly nature, enabling seamless adoption in future studies. - Compile a comprehensive report that highlights the achievements, methodologies, and outcomes of the study. - (Optional) Prepare a manuscript summarizing the project's findings for submission to a prominent and relevant academic journal, aiming to disseminate the research outcomes to the wider scientific community. **Your Benefits** - Gain unique access and first-hand experience in one of the leading institutions on long-term health management - At the Swiss Paraplegic Centre at Nottwil. - Working with the potential of long-term applications from the results of this thesis work. **Your Profile** - Proven understanding of signal analysis and pattern recognition. - Strong experience with Python (preferred) - Structured and reliable working style - Ability to work independently on a challenging topic.

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Published since: 2024-04-21 , Earliest start: 2024-05-19 , Latest end: 2024-12-31

Applications limited to Agroscope , Berner Fachhochschule , CERN , Corporates Switzerland , CSEM - Centre Suisse d'Electronique et Microtechnique , Department of Quantitative Biomedicine , Eawag , Empa , EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , Fernfachhochschule , Forschungsinstitut für biologischen Landbau (FiBL) , Friedrich Miescher Institute , Hochschulmedizin Zürich , IBM Research Zurich Lab , Institute for Research in Biomedicine , Lucerne University of Applied Sciences and Arts , NCCR Democracy , NGOs Switzerland , Pädagogische Hochschule St.Gallen , Paul Scherrer Institute , Physikalisch-Meteorologisches Observatorium Davos , Sirm Institute for Regenerative Medicine , Swiss Federal Institute for Forest, Snow and Landscape Research , Swiss Institute of Bioinformatics , Swiss National Science Foundation , SystemsX.ch , Università della Svizzera italiana , Université de Neuchâtel , University of Basel , University of Berne , University of Fribourg , University of Geneva , University of Lausanne , University of Lucerne , University of St. Gallen , University of Zurich , Wyss Translational Center Zurich , Zurich University of Applied Sciences , Zurich University of the Arts , University of Konstanz , Technische Universität München , TU Berlin , Eberhard Karls Universität Tübingen , European Molecular Biology Laboratory (EMBL) , FH Aachen , Humboldt-Universität zu Berlin , Justus Liebig University, Gießen , Ludwig Maximilians Universiy Munich , Martin Luther Universitat, Halle , Max Delbruck Center for Molecular Medicine (MDC) , Max Planck Society , Otto Von Guericke Universitat, Magdeburg , RWTH Aachen University , Social Science Research Center Berlin , Technische Universität Hamburg , TU Darmstadt , TU Dresden , Universität der Bundeswehr München , Universität Ulm , Universität zu Lübeck , University of Cologne , University of Erlangen-Nuremberg , University of Hamburg , Universtity of Bayreuth , Delft University of Technology , Maastricht Science Programme , Radboud University Nijmegen , Utrecht University , Max Planck ETH Center for Learning Systems , European Molecular Biology Laboratory , IEE S.A. Luxembourg , Istituto Italiano di Tecnologia , Technical University of Denmark , Technion - Israel Institute of Technology , University of Southern Denmark , Imperial College London , UCL - University College London , University of Oxford , University of Cambridge , National Institute for Medical Research

Organization Spinal Cord Injury & Artificial Intelligence Lab

Hosts Paez Diego, Dr. , Paez Diego, Dr.

Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology

Image Based Robust Pose Estimation for General Excavator Buckets

Robotic Systems Lab

The efficient operation of excavators in construction environments necessitates precise pose estimation of their buckets. Current methods rely on IMUs placed on the excavator arm which require tedious calibration and can be damaged during construction operations. This project aims to leverage computer vision and machine learning to enhance pose estimation, thereby enabling VR overlays for teleoperation and facilitating automation tasks.

Keywords

Computer Vision, Machine Learning, Synthetic Images, Excavators, Construction, 3D Pose Estimation

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Semester Project , Master Thesis

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Published since: 2024-04-16 , Earliest start: 2024-04-01

Organization Robotic Systems Lab

Hosts Schorp Vincent , Terenzi Lorenzo

Topics Information, Computing and Communication Sciences

Learning Robust Agile Flight via Adaptive Curriculum

Robotics and Perception

This project focuses on developing robust reinforcement learning controllers for agile drone navigation using adaptive curricula. Commonly, these controllers are trained with a static, pre-defined curriculum. The goal is to develop a dynamic, adaptive curriculum that evolves online based on the agents' performance to increase the robustness of the controllers.

Keywords

Reinforcement Learning, Drones

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Published since: 2024-04-08 , Earliest start: 2023-11-01 , Latest end: 2024-12-31

Organization Robotics and Perception

Hosts Xing Jiaxu

Topics Engineering and Technology

Design & Implementation of Wireless Video Transmission for Endoscopy

Multiscale Robotics Lab

In vivo wireless video transmission is cutting-edge but largely unexplored area of research promising to improve a wide variety of minimally-invasive procedures.

Keywords

Wireless video transmission Endoscopy Minimally-invasive procedures Capsule endoscopy Circuit design

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Semester Project , Internship , Master Thesis , Student Assistant / HiWi

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Published since: 2024-04-08 , Earliest start: 2024-04-15 , Latest end: 2024-08-31

Organization Multiscale Robotics Lab

Hosts Lyttle Sean

Topics Engineering and Technology

Deep Learning of Residual Physics For Soft Robot Simulation

Soft Robotics Lab

Incorporating state-of-the-art deep learning approaches to augment conventional soft robotic simulations for a fast, accurate and useful simulation for real soft robots.

Keywords

Soft Robotics, Machine Learning, Physical Modeling, Simulation

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Semester Project , Master Thesis

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Published since: 2024-04-03 , Earliest start: 2024-04-01 , Latest end: 2024-12-31

Organization Soft Robotics Lab

Hosts Michelis Mike , Katzschmann Robert, Prof. Dr.

Topics Information, Computing and Communication Sciences , Engineering and Technology

Learning Pose Estimation for Partially Occluded Objects from Simulation

Robotic Systems Lab

This project addresses the task of 6D pose estimation for general-purpose objects, particularly when dealing with occlusion. We aim to leverage recent deep learning methods and synthetic data generation schemes to enable robust object manipulation.

Keywords

Object Pose Estimation, Perceptive Manipulation, Photorealistic Simulation

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Semester Project , Master Thesis

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Published since: 2024-04-03

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne

Organization Robotic Systems Lab

Hosts Zurbrügg René , Bhardwaj Arjun , Patil Vaishakh

Topics Information, Computing and Communication Sciences

Interpretation of instrumented movement analysis in neurorehabilitation

Rehabilitation Engineering Lab

With advancing technology, healthcare professionals now have greater access to quantifying human movement, which will increasingly influence health assessments. However, interpreting movement data, particularly for individuals with neurological impairments, remains challenging. Our project aims to explore experts' insights on interpreting such data. Through multi-center focus groups, we gather healthcare professionals' perspectives to enhance informed decision-making in clinical settings.

Keywords

Neurorehabilitation, Focus Groups, Instrumented Movement Analysis

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Published since: 2024-04-02 , Earliest start: 2024-04-14 , Latest end: 2024-10-31

Organization Rehabilitation Engineering Lab

Hosts Mayrhuber Laura

Topics Medical and Health Sciences , Engineering and Technology , Behavioural and Cognitive Sciences

Assessing the feasibility of plantar pressure measurement devices for monitoring the diabetic population

Biomedical and Mobile Health Technology Lab

The goal of the project is to assess the feasibility of using commercially available plantar pressure monitoring devices (so called smart insoles) on the diabetic population. Pressure ulcers are a common complication of the diabetic foot, and monitoring plantar pressure continuously is a potential measure of prevention. Diabetic patients are often prescribed personalized footwear (e.g., curved insoles that accommodate any deformity in the feet). This project aims at assessing the potential of the smart insoles available on the market to monitor plantar pressure in diabetic patients with such custom footwear.

Keywords

wearables, mobile health, prevention, plantar pressure monitoring, diabetic foot

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Semester Project , Bachelor Thesis

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Published since: 2024-03-26 , Earliest start: 2024-04-08 , Latest end: 2024-09-02

Organization Biomedical and Mobile Health Technology Lab

Hosts Galli Valeria

Topics Medical and Health Sciences , Engineering and Technology

gpuFlightmare: High-Performance GPU-Based Physics Simulation and Image Rendering for Flying Robots

Robotics and Perception

gpuFlightmare: High-Performance GPU-Based Physics Simulation and Image Rendering for Flying Robots

Keywords

Reinforcement Learning, Robotics, Simulation, Vision-based Navigation

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Semester Project , Master Thesis

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Published since: 2024-03-14 , Earliest start: 2024-03-13

Organization Robotics and Perception

Hosts Song Yunlong

Topics Engineering and Technology

Autonomous Flight Using A Camera

Robotics and Perception

Fly like a bird

Keywords

Vision-based Flight, Autonomous Agents, Reinforcement Learning

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Semester Project , Master Thesis

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Published since: 2024-03-14 , Earliest start: 2024-03-13

Organization Robotics and Perception

Hosts Song Yunlong

Topics Engineering and Technology

Visual Representation Learning for Efficient Deep Reinforcement Learning

Robotics and Perception

Visual Representation Learning for Efficient Deep Reinforcement Learning

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Semester Project , Master Thesis

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Published since: 2024-03-14 , Earliest start: 2024-03-13

Organization Robotics and Perception

Hosts Song Yunlong

Topics Information, Computing and Communication Sciences , Engineering and Technology

Master Thesis/ Internship: Causal Machine Learning with Experts in the Loop for Spinal Cord Injury (SCI) Comorbitities

Sensory-Motor Systems Lab

Despite the growing amount of work on applying causal discovery method with expert knowledge to areas of interest, few of them inspect the uncertainty of expert knowledge (what if the expert goes wrong?). This is highly important since that in scientific fields, causal discovery with expert knowledge should be cautious and an approach taking expert uncertainty into account will be more robust to potential bias induced by individuals. Therefore, we aim to develop an iterative causal discovery method with experts in the loop to enable continual interaction and calibration between experts and data. Based on the qualifications of the candidates, we can arrange a subsidy/allowance for covering traveling or living costs.

Keywords

Causal Discovery, Expert Knowledge, Iterative Algorithm, Spinal Cord Injury

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Semester Project , Internship , Master Thesis

Project Background

Causal graphical models (CGM) can be determined via randomized controlled experiments (and this is the golden standard to determine the causal relations and quantify the causal effects), learned purely from data[1](i.e., structural learning or causal discovery) or elicited by domain experts based on their knowledge and experiences. It is also feasible to learn the CGM from data with prior expert knowledge when data become not reliable due to insufficient sample size or measurement errors. Early works in causal discovery utilized constraint-based approaches, such as the PC algorithm and the FCI algorithm. These methods exploit conditional independence relationships among variables to infer causal structures. They can restrict the output graph space through conditional independence assumptions or background knowledge about causal relationships [2]. Score-based approaches, such as the GES (Greedy Equivalence Search) algorithm, aim to find the best causal structure that optimizes a scoring criterion, e.g., the BIC (Bayesian Information Criterion) score. These methods often incorporate expert knowledge through prior specifications or domain-specific constraints on the model parameters [3, 4, 5]. Recent advancements in causal discovery involve hybrid approaches that combine elements of constraint-based and score-based methods. These approaches aim to leverage the strengths of both approaches to improve the accuracy and efficiency of causal structure learning. They may incorporate expert knowledge through the integration of domain-specific constraints and scoring functions. In addition to observational data, causal discovery methods have also been developed to handle experimental data, where interventions or randomized experiments are conducted. These methods utilize the causal effects of interventions to infer causal relationships. Expert knowledge can be incorporated by designing well-controlled experiments or interventions based on domain expertise. Despite the growing amount of work on structural learning with expert knowledge in scientific fields, few of them inspect the uncertainty of expert knowledge (what if the expert goes wrong?). This is highly important since in medical applications, causal discovery with expert knowledge should be taken cautiously and sometimes experts can give false knowledge, leading to serious mistakes. An approach considering the expert uncertainty during CGM construction will be more robust to potential bias induced by individuals. This idea is made possible by recent progress toward evaluating and falsifying a given CGM from observational data without ground truth[6]. Therefore, we hope to develop an iterative causal discovery method with experts in the loop for continual interaction and calibration between experts and data. In the meantime, we are also trying to develop an interactive user interface for medical experts to input their knowledge and edit the learned graph. References [1] Glymour, C., Zhang, K., & Spirtes, P. (2019). Review of causal discovery methods based on graphical models. Frontiers in genetics, 10, 524. [2] Flores, M. J., Nicholson, A. E., Brunskill, A., Korb, K. B., & Mascaro, S. (2011). Incorporating expert knowledge when learning Bayesian network structure: a medical case study. Artificial intelligence in medicine, 53(3), 181-204. [3] O’Donnell, R. T., Nicholson, A. E., Han, B., Korb, K. B., Alam, M. J., & Hope, L. R. (2006). Causal discovery with prior information. In AI 2006: Advances in Artificial Intelligence: 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, December 4-8, 2006. Proceedings 19 (pp. 1162-1167). Springer Berlin Heidelberg. [4] Perković, E., Kalisch, M., & Maathuis, M. H. (2017). Interpreting and using CPDAGs with background knowledge. arXiv preprint arXiv:1707.02171. [5] Kleinegesse, S., Lawrence, A. R., & Chockler, H. (2022). Domain Knowledge in A*-Based Causal Discovery. arXiv preprint arXiv:2208.08247. [6] Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. arXiv preprint arXiv:2305.09565.

Your Task

1. Literature Review on Causal Discovery: You will study classic and state-of-the-art causal discovery methods (preferably on real datasets with mixed-type variables) that incorporate expert knowledge, leading to a systematic review of causal discovery methods in different categories (constraint-based vs. score-based) with different assumptions (e.g., latent variables, causal mechanism/function forms, and cycles) for different settings (e.g., static data or time series). 2. Data Exploration and Analysis: You will explore analyze and datasets collected for HAPI (Hospital-Acquired Pressure Injury) and Hospital Stay after PI Surgeries. The data record hundreds of patients into dozens of mixed-type variables including patients’ demographic features, lab testing values, and health conditions observed during their stay. Apart from clinical data, you will also have access to multivariate time series extracted from bio-signals on SCI people under intervention experiments. We aim to learn the causal relations among these variables of interest from the above data with experts’ knowledge. Additionally, we will probe some public datasets of other fields such as heart diseases and earth science with ground-truth graphs. 3. Methodology Development and Deployment: You will help develop and validate an iterative algorithm for causal discovery with the expert in the loop against benchmarks using the simulated and real datasets. There are two potential exploring directions: 1. How will the correct expert knowledge help reduce the search space of graphs to improve the accuracy and efficiency of the algorithm? 2. How to encode prior knowledge or design a post-modification strategy with uncertainty from experts’ input to increase the robustness of the algorithm? Based on the methodology, an interactive web/application interface for doctors to input their knowledge and edit the learned graph is expected. If possible, you will assist in deriving the mathematical guarantee for the iterative algorithm in terms of convergence and stability properties. 4. Presentation and Documentation: You will prepare a high-quality manuscript for publication with a clear and engaging presentation of the results and methodology.

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Published since: 2024-03-13 , Earliest start: 2024-04-15 , Latest end: 2024-10-15

Organization Sensory-Motor Systems Lab

Hosts Paez Diego, Dr. , Paez Diego, Dr.

Topics Medical and Health Sciences , Mathematical Sciences , Information, Computing and Communication Sciences

Development of a novel actuator for soft-robotic applications

Multiscale Robotics Lab

The Multiscale Robotics Lab develops novel actuation methods for endoscopic devices utilizing magnetic navigation systems. Currently, an external magnetic field can steer permanent magnets through the Euclidean space. Although the gradients of the external magnetic field can be controlled, the actuation of the magnetic continuum robot (m-CR) is still limited. This problem can be overcome by actively re-magnetizing the magnets positioned in the m-CR. The pulse-magnetizer’s driving circuit and magnetization feedback sensors needs to be minimized to fulfill the requirements for medical endoscopic devices.

Keywords

Feedback Control, Actuator Design, Magnetic Hysteresis Modeling, Soft Robotics

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Semester Project , Master Thesis

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Published since: 2024-03-11 , Earliest start: 2024-03-01 , Latest end: 2024-08-31

Organization Multiscale Robotics Lab

Hosts Ehmke Claas

Topics Information, Computing and Communication Sciences , Engineering and Technology

Deploying Locomotion Policies Trained in Differentiable Simulation on Real Hardware

Robotic Systems Lab

In recent years, using deep Reinforcement Learning (RL) for robotic motion policies has demonstrated impressive performance, yielding unprecedented robustness on real hardware. Current sim2real approaches rely on large-scale pre-training with domain randomization to make policies robust but struggle with high-dimensional spaces. Current RL methods are primarily limited by their low sample efficiency. Leveraging differentiable simulators for first-order gradient information shows great results for enhancing sample efficiency. Although promising simulation results exist, deployment on hardware is not usually done. The goal of this thesis is to train quadrupedal locomotion policies in a differentiable simulation framework, and then enable real-world deployment by modifying the simulation, the policy training, or the learning algorithm. Ideally, we can leverage properties of differentiable simulators in this process to improve sim2real transfer by fitting real data.

Keywords

Deep Reinforcement Learning, Differentiable Simulation, Quadrupedal Locomotion Control, Sim2Real

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Published since: 2024-03-11

Organization Robotic Systems Lab

Hosts Klemm Victor

Topics Information, Computing and Communication Sciences , Engineering and Technology

Towards AI Safety: Adversarial Attack & Defense on Neural Controllers

Robotic Systems Lab

The project is collaborating between SRI and RSL/CRL lab and aims to investigate the weakness of the neural controller based on the state-of-the-art [3] attacking method.

Keywords

Adversarial attack; safe AI; Reinforcement learning

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Published since: 2024-03-06 , Earliest start: 2024-03-06 , Latest end: 2024-09-30

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne

Organization Robotic Systems Lab

Hosts Shi Fan , Shi Fan , Shi Fan

Topics Information, Computing and Communication Sciences , Engineering and Technology

Physics-informed machine learning in microfluidics

Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Understanding the distribution and mechanics of velocity and pressure within microaneurysms is crucial for controlling microrobots navigating through them. Traditional methods for velocity and pressure measurement in microchannels, such as particle image velocimetry (PIV) and numerical simulations based on fluidic physics laws, suffer from high computational demands and inability to operate in real-time. Moreover, pure image methods struggle with near-wall regions lacking visible particles. Leveraging recent advancements in machine learning, particularly convolutional neural networks (CNNs), this project proposes a novel approach - a physics-informed CNN integrated with Navier-Stokes equations and optical flow equations. This CNN aims to accurately predict velocity and pressure profiles in microchannel flows in real-time using only flow images and essential physical parameters. The network architecture comprises an encoder-decoder structure with seven convolutional layers, incorporating down-sampling and up-sampling layers. The final output layer produces three channels representing horizontal velocity, vertical velocity, and pressure. Additionally, a physics-informed loss function, incorporating dimensionless Navier-Stokes equation residuals and optical flow equation residuals, enhances the model's performance by integrating knowledge of fluid dynamics and computer vision. This approach represents a promising advancement towards achieving real-time, high-accuracy prediction of velocity and pressure fields in microchannel flows, with potential applications in microrobotics and microfluidics.

Keywords

artificial Intelligence, physics-informed machine learning, microfluidics, fluid dynamics

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Published since: 2024-03-05 , Earliest start: 2023-02-09 , Latest end: 2024-09-22

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Medany Mahmoud

Topics Information, Computing and Communication Sciences , Engineering and Technology , Physics

Learning Diverse Adversaries to Black-box Learning-based Controller for Quadruped Robots

Robotic Systems Lab

The project aims to leverage the latest unsupervised skill discovery techniques to validate the state-of-the-art black-box learning-based controllers in diverse ways.

Keywords

Diversity in RL, Trustworthy AI

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Published since: 2024-03-02 , Earliest start: 2024-03-02 , Latest end: 2024-08-28

Applications limited to ETH Zurich , [nothing]

Organization Robotic Systems Lab

Hosts Shi Fan , Shi Fan , Shi Fan

Topics Information, Computing and Communication Sciences

Autonomous Mapping for Construction Site Monitoring

Autonomous Systems Lab

Autonomous mapping systems can provide crucial information for construction monitoring and industrial inspection tasks. They aim to repeatedly map known environments with unknown partial modifications without human intervention.

Keywords

Drone, Computer vision, Path Planning, Mapping

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Published since: 2024-02-29 , Earliest start: 2024-01-01 , Latest end: 2024-07-01

Organization Autonomous Systems Lab

Hosts Pinto Teixeira Lucas

Topics Information, Computing and Communication Sciences , Engineering and Technology

Towards interpretable learning pipeline: A visual-assisted workflow for locomotion learning

ETH Competence Center - ETH AI Center

Current reinforcement learning (RL)-based locomotion controllers have shown promising performance. However we are still not clear about what is learned during the training process. In this project, we investigate the proper metrics and visualisation techniques to interactively steer the locomotion learning tasks.

Keywords

Reinforcement learning; visualization; interpretable AI

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Published since: 2024-02-28 , Earliest start: 2024-02-26 , Latest end: 2024-08-26

Organization ETH Competence Center - ETH AI Center

Hosts Zhang Xiaoyu , Shi Fan , Wang April , Shi Fan , Shi Fan

Topics Engineering and Technology

Temporal Graphical Modeling for Understanding and Preventing Autonomic Dysreflexia

Spinal Cord Injury & Artificial Intelligence Lab

This project will be based on the preliminary results obtained from a previous master project in causal graphical modeling of autonomous dysreflexia (AD). The focus of the extension would be two-fold. One is improving the temporal graphical reconstruction for understanding the mechanism of AD. The other one is building a forecasting framework for the early detection and prevention of AD using the graph structure we constructed before. Please refer to the attached document for more details about the task description. Based on the candidate's qualifications, funding/allowance can be provided.

Keywords

Graphical Modeling; Graph Neural Networks; Multivariate Time Series; Spinal Cord Injuries; Autonomic Dysreflexia; Wearable Sensing

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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)

Description

Graphical modeling (GM) refers to a statistical modeling framework that uses probabilistic graphs to encode conditional dependencies and independences among variables of interest [1]. Due to its ability to represent complex probability distributions, GM has been developed and adapted to various fields including earth science [2], biology [3], health [4], and medicine [5] to dissect the structure of targeted phenomena to improve the understanding of the mechanism and decision-making process. Multivariate Time series (MTS), characterized by sequential observations of several variables over time, is pervasive in various disciplines. Understanding variables' temporal evolution and spatial dependencies is essential for uncovering patterns, trends, and anomalies that unfold over time. Time series analysis provides insights into the underlying dynamics of phenomena, enabling researchers and practitioners to make informed predictions, learn temporal dependencies, and formulate effective strategies. The exploration of temporal graphical models (TGMs) in the realm of time series analysis is paramount, given the crucial role that time series data plays in various fields. In this context, TGMs emerge as a sophisticated approach to modeling time series data, capturing the dynamic and evolving relationships among variables. As a validation scenario, we are working with the Swiss Paraplegic Centre (SPZ) to improve the long-term prognosis of individuals with spinal cord injury (SCI). Secondary health conditions (SHC) such as pulmonary infections, pressure injuries (PI), AD, and cardiovascular dysregulation are highly prevalent among the SCI population, imposing a tremendous risk on the patient’s health and the healthcare system [6,7]. Through the medical records and multimodal-sensory monitoring system, we expect to develop an intelligent system that can fuse different data sources, interact with clinical operation and expertise, and guide the medical treatment and decision-making for SHC management of SCI individuals. This project will focus on the TGM of a multivariate time series extracted from a wearable multi-modal sensing system. We aim to learn the high-resolution temporal dynamics together with spatial dependencies among biometrics, based on which the early detection and inference of AD episodes could be enabled. For more details about the task description, please refer to the attached document!

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Published since: 2024-02-28 , Earliest start: 2024-04-01 , Latest end: 2024-10-01

Organization Spinal Cord Injury & Artificial Intelligence Lab

Hosts Paez Diego, Dr. , Li Yanke , Paez Diego, Dr. , Paez Diego, Dr.

Topics Medical and Health Sciences , Information, Computing and Communication Sciences

Disease Onset Forecasting through Graphical Modeling Based Digital Twin from Biomedical Data for Spinal Cord Injury Individuals

Spinal Cord Injury & Artificial Intelligence Lab

This project focuses on developing an explainable Artificial Intelligence (xAI) framework based on graphical modeling (GM), to enhance the capacity and capability of medical AI. Collaborating with the Swiss Paraplegic Centre (SPZ) for validation, our goal is to improve the long-term prognosis of spinal cord injury (SCI) individuals. Through medical records and a multimodal sensory monitoring system, we aim to create digital twins capable of integrating diverse data sources, guiding medical treatment, and addressing common secondary health conditions in the SCI population. The envisioned GM-based digital twin (GMDT) will represent hierarchical relations across demographic features, functional abilities, daily activities, and health conditions for SCI individuals, allowing for downstream tasks such as prediction, causal inference, and counterfactual reasoning. The assimilation and evolution between the physical and digital twins will be implemented under the GM framework, promising advancements in personalized healthcare strategies and improved outcomes for SCI people. Please refer to the attached document for more details about the task description. Based on the candidate's qualifications, funding/allowance can be provided.

Keywords

Graphical Modelling, Digital Twins, Causal Inference, Data Fusion, Multimodal Learning, Physiological Modelling, Spinal Cord Injuries, Digital Healthcare

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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)

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Graphical modeling (GM) was originally proposed as a statistical modeling framework that uses probabilistic graphs to encode conditional dependences and independence among variables of interest [1]. It is straightforward for stakeholders to understand the interaction among different factors and reasons using the structure and relations. Due to the strong ability of GM to represent complex probability distributions, it has been developed and adapted to various fields including earth science [2], biology [3], and healthcare [4] to dissect the structure and sparsity of targeted phenomenon to improve the understanding of mechanism and decision-making process. In digital healthcare and medicine with a high-stake nature, it is imperative to enable a transparent and accessible modeling paradigm such that both modeling technicians and medical experts can comprehend, trust, and improve the digital models for decision-making. Given the transparent structural representation of GM, we aim to develop an explainable Artificial intelligence (xAI) framework based on CGM (Causal Graphical Models, a special type of GM where edges represent causal relations) to cope with the challenges of real-world medical data and improve both the capacity and capability of medical AI. As a validation scenario of our approach, we are working with the Swiss Paraplegic Centre (SPZ) to improve the long-term prognosis of individuals with spinal cord injury (SCI). Secondary health conditions such as pulmonary infections, pressure injuries (PI), autonomic dysreflexia (AD), and cardiovascular dysregulation are highly prevalent among the SCI population, imposing a tremendous risk on the patient’s health and the healthcare system [5]. Through the medical records and multimodal-sensory monitoring system, we expect to end up with the digital twins of SCI individuals that can fuse different data sources, interact with clinical operations and expertise, and guide medical treatment. Your Tasks: 1. Literature Review on Digital Twins and Graphical Models: You will study literature related to graphical models with applications, digital twins with applications, and preferably the combination of both fields (GMDT). 2. Data Exploration and Analysis: You will explore multimodal datasets collected for PI and AD. The PI data record the conditions of hundreds of SCI individuals during their hospital stay. There are mixed-type variables including demographic features, lab testing values, and health conditions observed during their stay. Additionally, you will have access to multivariate time series extracted from bio-signals on SCI people under intervention experiments. 3. Methodology Development and Deployment: You will help define what data to use, design the experiments, develop, and validate both the modeling foundation and implementation pipeline for the GMDT. A graphical user interface is expected to be built based on the current framework we are developing in the lab. 4. Presentation and Documentation: You will prepare a high-quality manuscript for publication with a clear and engaging presentation of the methodology and validation results. References: [1] Glymour, C., Zhang, K. & Spirtes, P. Review of Causal Discovery Methods Based on Graphical Models. Front. Genet. 10, 524 (2019). [2] Runge, J. et al. Inferring causation from time series in Earth system sciences. Nat. Commun. 10, 2553 (2019). [3] Friedman, N. Inferring Cellular Networks Using Probabilistic Graphical Models. Science 303, 799–805 (2004). [4] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nat. Commun. 11, 3923 (2020). [5] Haisma, J. et al. Complications following spinal cord injury: occurrence and risk factors in a longitudinal study during and after inpatient rehabilitation. J. Rehabil. Med. 39 5, 393–8 (2007).

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Published since: 2024-02-28 , Earliest start: 2024-03-15 , Latest end: 2024-09-30

Applications limited to TU Dresden , TU Darmstadt , TU Berlin , Technische Universität München , Technische Universität Hamburg , RWTH Aachen University , Max Planck Society , Ludwig Maximilians Universiy Munich , Humboldt-Universität zu Berlin , Eberhard Karls Universität Tübingen , Universität zu Lübeck , Imperial College London , UCL - University College London , University of Oxford , University of Cambridge , Delft University of Technology , Zurich University of Applied Sciences , Wyss Translational Center Zurich , University of Zurich , Swiss Institute of Bioinformatics , IBM Research Zurich Lab , ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa , Corporates Switzerland , Zurich University of the Arts , University of St. Gallen , University of Lausanne , University of Geneva , University of Fribourg , University of Berne , University of Basel , Swiss National Science Foundation , Swiss Federal Institute for Forest, Snow and Landscape Research , Paul Scherrer Institute , CERN , Department of Quantitative Biomedicine , Eawag , University of Konstanz , University of Cologne , University of Erlangen-Nuremberg , University of Hamburg , Universtity of Bayreuth , Universität Ulm , Universität der Bundeswehr München , Social Science Research Center Berlin , National Institute for Medical Research , Royal College of Art , University of Leeds , University of Manchester , University of Nottingham , University of Aberdeen , Utrecht University , Radboud University Nijmegen , Maastricht Science Programme , Stanford University , Yale University , CNRS - Centre national de la recherche scientifique , Massachusetts Institute of Technology , Max Planck ETH Center for Learning Systems , The University of Tokyo , Tsinghua University , Peking University , Politecnico di Milano , Princeton University , Harvard , University of Toronto , University of Copenhagen , University of California, Berkeley , The University of Edinburgh , Technical University of Denmark , The University of Melbourne , The Australian National University , National University of Singapore , Nanyang Technological University

Organization Spinal Cord Injury & Artificial Intelligence Lab

Hosts Li Yanke , Paez Diego, Dr. , Paez Diego, Dr.

Topics Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences

Microrobot manipulation and ultrasound imaging

Acoustic Robotics for Life Sciences and Healthcare (ARSL)

We want to expand the use of acoustic microrobots for biomedical applications by studying their manipulation in 3D environments and their simultaneous real time tracking using non-invasive ultrasound imaging.

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Microrobots, Ultrasounds, 3D manipulation, Imaging

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Semester Project , Bachelor Thesis , Master Thesis , Summer School

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Published since: 2024-02-27 , Earliest start: 2024-01-01 , Latest end: 2024-10-31

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Del Alexia

Topics Engineering and Technology

Acoustic microrobots navigation in tumor vasculature model

Acoustic Robotics for Life Sciences and Healthcare (ARSL)

We want to expand the use of acoustic microrobots for drug delivery applications in tumor environments.

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Microfluidics, drug delivery, Microrobots, Ultrasound, Biomedical

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Semester Project , Collaboration , Bachelor Thesis , Master Thesis

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Published since: 2024-02-27 , Earliest start: 2024-02-01 , Latest end: 2024-10-31

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Del Alexia

Topics Engineering and Technology

Fabrication of self-growing magnetic micro-catheters

Multiscale Robotics Lab

This project aims on the development of a magnetically guidable self-growing micro-catheter.

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Microrobotics, Self-assembly, Polymers, Nanoparticle

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Semester Project , Master Thesis

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Published since: 2024-02-27 , Earliest start: 2024-02-28 , Latest end: 2024-04-30

Applications limited to ETH Zurich

Organization Multiscale Robotics Lab

Hosts Hertle Lukas

Topics Engineering and Technology

End-to-end Leaning Terrain Cost with Robot Kinematic Constraints

Robotic Systems Lab

Navigating the unpredictable off-road environment, autonomous robots require a tailored approach to overcome obstacles and optimize pathfinding. Our proposed terrain cost mapping system goes beyond traditional processing by factoring in each robot's specific kinematic abilities. We introduce a novel simulation-based Roll-Out technique to predict a robot's stability over varied terrains, thereby calculating a precise terrain cost. This innovative strategy promises to enhance autonomous navigation by ensuring safe and efficient traversal tailored to individual robotic capabilities.

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Leaning Terrain Cost; Off-road Navigation; Robot Kinematics

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Semester Project , Master Thesis

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Published since: 2024-02-21 , Earliest start: 2023-11-30 , Latest end: 2024-01-31

Organization Robotic Systems Lab

Hosts Yang Fan

Topics Information, Computing and Communication Sciences

Foundation Model for Drone Navigation in Confined Spaces

Robotics and Perception

This master thesis project centers on the development of a foundation model for drone navigation within confined spaces such as ballast tanks of ships.

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Semester Project , Master Thesis

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Published since: 2024-02-20 , Earliest start: 2024-03-01 , Latest end: 2025-03-01

Organization Robotics and Perception

Hosts Khambhaita Harmish

Topics Information, Computing and Communication Sciences

Autonomously traversing ship manholes using end-to-end vision-based control

Robotics and Perception

Develop an end-to-end learning-based approach for autonomous drone navigation in ship ballast tank manholes, incorporating both real and simulated training data. The project aims to emphasize speed, a high success rate, and safety in flying through the confined spaces of ship interiors.

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Published since: 2024-02-20 , Earliest start: 2024-03-01 , Latest end: 2025-03-01

Organization Robotics and Perception

Hosts Khambhaita Harmish

Topics Information, Computing and Communication Sciences

Segmentation and Object Detection in Neural Radiance Fields (NeRFs) for Enhanced 3D Scene Understanding

Robotics and Perception

This master thesis project focuses on advancing 3D scene understanding through the integration of segmentation and object detection techniques within Neural Radiance Fields (NeRFs).

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Published since: 2024-02-20 , Earliest start: 2024-03-01 , Latest end: 2025-03-01

Organization Robotics and Perception

Hosts Khambhaita Harmish

Topics Information, Computing and Communication Sciences

Vision-Based Autonomous Drone Recovery Using Reinforcement Learning

Robotics and Perception

This project is focused on developing a vision-only flight recovery system for autonomous drones. A critical capability for autonomous drones is to recover safely from any unstable state. This project explores the potential of using reinforcement learning to enable a drone to transition from an unstable to a stable state, using only vision sensors. The challenge lies in creating a system that not only stabilizes the drone but also ensures it can safely land in various unforeseen scenarios.

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Robotics, Autonomous Systems, Control, Quadcopter

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Semester Project , Master Thesis

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Published since: 2024-02-20 , Earliest start: 2024-02-19 , Latest end: 2025-07-16

Organization Robotics and Perception

Hosts Romero Angel

Topics Information, Computing and Communication Sciences , Engineering and Technology

Learning Rapid UAV Exploration with Foundation Models

Robotics and Perception

Recent research has demonstrated significant success in integrating foundational models with robotic systems. In this project, we aim to investigate how these foundational models can enhance the vision-based navigation of UAVs. The drone will utilize learned semantic relationships from extensive world-scale data to actively explore and navigate through unfamiliar environments. While previous research primarily focused on ground-based robots, our project seeks to explore the potential of integrating foundational models with aerial robots to enhance agility and flexibility.

Keywords

Visual Navigation, Foundation Models, Drones

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Semester Project , Master Thesis

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Published since: 2024-02-20 , Earliest start: 2024-01-25 , Latest end: 2024-12-31

Organization Robotics and Perception

Hosts Xing Jiaxu

Topics Engineering and Technology

Autonomous Drone Navigation via Learning from YouTube Videos

Robotics and Perception

Inspired by how humans learn, this project aims to explore the possibility of learning flight patterns, obstacle avoidance, and navigation strategies by simply watching drone flight videos available on YouTube.

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Semester Project , Master Thesis

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Published since: 2024-02-19 , Earliest start: 2024-02-19 , Latest end: 2025-02-19

Applications limited to ETH Zurich , University of Zurich

Organization Robotics and Perception

Hosts Cannici Marco

Topics Information, Computing and Communication Sciences

Gaussian Splatting Visual Odometry

Robotics and Perception

Gaussian Splatting Visual Odometry

Keywords

Computer Vision, Visual Odometry

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Semester Project , Master Thesis

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Published since: 2024-02-19 , Earliest start: 2024-02-19 , Latest end: 2024-12-01

Organization Robotics and Perception

Hosts Cioffi Giovanni

Topics Engineering and Technology

Navigating on Mars

Robotics and Perception

The first ever Mars helicopter Ingenuity flew over a texture-poor terrain and RANSAC wasn’t able to find inliers: https://spectrum.ieee.org/mars-helicopter-ingenuity-end-mission Navigating the Martian terrain poses significant challenges due to its unique and often featureless landscape, compounded by factors such as dust storms, lack of distinct textures, and extreme environmental conditions. The absence of prominent landmarks and the homogeneity of the surface can severely disrupt optical navigation systems, leading to decreased accuracy in localization and path planning.

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VIO, SLAM, Computer vision

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Published since: 2024-02-19 , Earliest start: 2024-02-26 , Latest end: 2024-06-30

Organization Robotics and Perception

Hosts Muglikar Manasi

Topics Engineering and Technology

Data-driven Event Generation from Images

Robotics and Perception

In this project, the student applies concepts from current advances in image generation to create artificial events from standard frames. Multiple state-of-the-art deep learning methods will be explored in the scope of this project.

Keywords

Computer Vision, Event Cameras, Deep Learning

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Semester Project , Master Thesis

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Published since: 2024-02-19 , Earliest start: 2024-02-19

Organization Robotics and Perception

Hosts Messikommer Nico

Topics Information, Computing and Communication Sciences

Efficient Neural Scene Reconstruction with Event Cameras

Robotics and Perception

This project seeks to leverage the sparse nature of events to accelerate the training of radiance fields.

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Semester Project , Master Thesis

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Published since: 2024-02-19 , Earliest start: 2024-02-19 , Latest end: 2025-02-19

Organization Robotics and Perception

Hosts Cannici Marco

Topics Information, Computing and Communication Sciences

Multimodal Fusion for Enhanced Neural Scene Reconstruction Quality

Robotics and Perception

The project aims to explore how prior 3D information can assist in reconstructing fine details in NeRFs and how the help of high-temporal resolution data can enhance modeling in the case of scene and camera motion.

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Published since: 2024-02-19 , Earliest start: 2024-02-19 , Latest end: 2025-02-19

Organization Robotics and Perception

Hosts Cannici Marco

Topics Information, Computing and Communication Sciences

Domain Transfer between Events and Frames for Motor Policies

Robotics and Perception

The goal of this project is to develop a shared embedding space for events and frames, enabling the training of a motor policy on simulated frames and deployment on real-world event data.

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Computer Vision, Event Cameras, Robotics, Unsupervised Domain Adaption

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Published since: 2024-02-19 , Earliest start: 2024-02-19

Organization Robotics and Perception

Hosts Messikommer Nico

Topics Information, Computing and Communication Sciences

Data-driven Keypoint Extractor for Event Data

Robotics and Perception

This project focuses on enhancing camera pose estimation by exploring a data-driven approach to keypoint extraction, leveraging recent advancements in frame-based keypoint extraction techniques.

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Computer Vision, Event Cameras, Keypoint Extraction, Visual Odometry

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Semester Project , Master Thesis

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Published since: 2024-02-19 , Earliest start: 2024-02-19

Organization Robotics and Perception

Hosts Messikommer Nico

Topics Information, Computing and Communication Sciences

Low Latency Occlusion-aware Object Tracking

Robotics and Perception

Low latency Occlusion-aware object tracking

Keywords

Computer Vision, Virtual Reality, Object tracking

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Semester Project , Master Thesis

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Published since: 2024-02-19 , Earliest start: 2024-02-19 , Latest end: 2024-12-01

Organization Robotics and Perception

Hosts Cioffi Giovanni

Topics Engineering and Technology

Event-based occlusion removal

Robotics and Perception

Unwanted camera occlusions, such as debris, dust, raindrops, and snow, can severely degrade the performance of computer-vision systems. Dynamic occlusions are particularly challenging because of the continuously changing pattern. This project aims to leverage the unique capabilities of event-based vision sensors to address the challenge of dynamic occlusions. By improving the reliability and accuracy of vision systems, this work could benefit a wide range of applications, from autonomous driving and drone navigation to environmental monitoring and augmented reality.

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Event cameras, occlusion, weather, computer vision

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Published since: 2024-02-19 , Earliest start: 2024-02-26 , Latest end: 2024-06-30

Organization Robotics and Perception

Hosts Muglikar Manasi

Topics Engineering and Technology

Bioinspired Ultrasound Microrobots

Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Inspired by naturally-occurring microswimmers such as spermatozoa that exploit the nonslip boundary conditions of a wall, we propose here a microrobot design (a “sperm-bot”) that can execute upstream motility triggered by ultrasound.

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Robotics, micro and nanorobots, microrobotics, soft robotics ultrasound, bioinspired

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Semester Project , Bachelor Thesis , Master Thesis , Summer School

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Published since: 2024-02-15 , Earliest start: 2024-10-01 , Latest end: 2024-12-19

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Ahmed Daniel, Prof.

Topics Engineering and Technology

Embedded algorithms of IMUs in a neurorehabilitation device

Rehabilitation Engineering Lab

The goal of this project is to help develop embedded firmware for a imu based rehabilitation device. This project is part of the SmartVNS project which utilizes movement-gated control of vagus nerve stimulation for stroke rehabilitation.

Keywords

electrical engineering PCB Embedded systems neurorehabilitation

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Published since: 2024-02-14 , Earliest start: 2024-01-06 , Latest end: 2024-12-31

Organization Rehabilitation Engineering Lab

Hosts Donegan Dane , Viskaitis Paulius

Topics Medical and Health Sciences , Engineering and Technology

Development of a Clinically Usable Electrode for tVNS

Rehabilitation Engineering Lab

This project aims to develop a clinically usable electrode for transcutaneous vagus nerve stimulation (tVNS) therapy. The objective is to create an electrode that is biocompatible, low-impedance, and easy to use, allowing patients to apply it themselves with minimal setup time. The project involves conducting a literature review, evaluating existing designs, selecting appropriate materials, developing a prototype, and assessing its efficacy and usability in a clinical setting. The outcome will be an electrode that enhances the convenience and effectiveness of tVNS therapy, contributing to improved patient treatment adherence and outcomes.

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Mechanical engineering, materials engineering, 3D modeling, anatomical modeling, CAD.

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Semester Project , Internship , Master Thesis

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Published since: 2024-02-14 , Earliest start: 2023-06-01

Organization Rehabilitation Engineering Lab

Hosts Viskaitis Paulius , Donegan Dane

Topics Engineering and Technology

Activity and fatigue detection using machine learning based on real-world data from smart clothing

Biomedical and Mobile Health Technology Lab

The aim of this project is to use machine learning methods to extract useful information such as activity type and fatigue level from real-world data acquired from our textile-based wearable technology during sport activities.

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smart clothing, wearable technology, textile sensor, fitness tracking, sports medicine, fatigue, machine learning, artificial intelligence, computer science

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2024-02-14 , Earliest start: 2023-09-15 , Latest end: 2024-05-31

Organization Biomedical and Mobile Health Technology Lab

Hosts Ahmadizadeh Chakaveh

Topics Information, Computing and Communication Sciences , Engineering and Technology

Multisensory assessment of physiological markers during neural stimulation for stroke rehabilitation

Rehabilitation Engineering Lab

Project goal is to assess outcomes of a non-invasive brain stimulator for future application in stroke rehabilitation. This will involve using an exciting novel method of brain stimulation together with simultaneous multisensory recordings of various physiological parameters, including heart rate, galvanic skin response, pupillometry and electroencephalogram (EEG). The results of the project will help develop brain stimulation protocols that elicit meaningful neural responses in healthy subjects, and in stroke patients.

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neural stimulation, neural biomarkers, neurophysiology, physiology, neuroscience, EEG, pupillometry

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Published since: 2024-02-14 , Earliest start: 2023-06-01

Organization Rehabilitation Engineering Lab

Hosts Viskaitis Paulius , Donegan Dane

Topics Engineering and Technology

Real-time control of neural stimulation for stroke patients.

Rehabilitation Engineering Lab

Real-time analysis of movement kinematics can benefit multiple different strategies in rehabilitation after stroke, including allowing closed-loop brain stimulation. Use of inertial measurement units (IMUs) allows detection of movement and extraction of kinematic features, but application in real-time remains challenging. This project will develop algorithms for real-time movement data analysis and feature extraction in typical rehabilitation tasks and general real-life movements. In turn, these algorithms will be applied to control novel brain stimulation approaches in stroke neurorehabilitation.

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Inertial measurement unit, IMU, movement tracking, machine learning, real-time, signal processing

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Published since: 2024-02-14 , Earliest start: 2023-05-09

Organization Rehabilitation Engineering Lab

Hosts Viskaitis Paulius , Donegan Dane

Topics Engineering and Technology

Develop software for wearable technologies

Biomedical and Mobile Health Technology Lab

The aim of this project is to develop mobile software to communicate with our already developed textile-based wearable technology and process sensor data for movement monitoring.

Keywords

smart clothing, wearable technology, software development, fitness tracking, sports medicine, mobile application, computer science

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2024-02-14 , Earliest start: 2023-09-15 , Latest end: 2024-05-31

Organization Biomedical and Mobile Health Technology Lab

Hosts Ahmadizadeh Chakaveh

Topics Information, Computing and Communication Sciences , Engineering and Technology

Efficient data processing and reporting in stroke neuro-rehabilitation

Rehabilitation Engineering Lab

Project goal is to optimise existing and develop new algorithms into an efficient system for signal pre-processing, data storage, analysis and visualization in motor-neurorehabilitation. This data is generated by stroke patients wearing motion sensors during their therapy sessions. Key endpoint of the project is to display real-time and longitudinal therapy results, which can aid therapists and patients. The results of the project will help develop a more efficient therapy and is a key part of a larger project that seeks to develop an intelligent and closed-loop neural stimulation system for stroke rehabilitation.

Keywords

health biomarkers, data science, computer science, data visualization, data processing, real-time, internet of medical things, IoMT, healthcare internet of things, healthcare IoT

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Semester Project , Internship , Master Thesis

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Published since: 2024-02-14 , Earliest start: 2023-05-09

Organization Rehabilitation Engineering Lab

Hosts Donegan Dane , Viskaitis Paulius

Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology

Design data acquisition solution for smart clothing

Biomedical and Mobile Health Technology Lab

The aim of this project is to develop and improve wearable electronics solutions for data acquisition from textile-based sensors used in our smart clothing.

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smart clothing, wearable technology, textile sensor, fitness tracking, sports medicine, PCB, electronics, computer science

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2024-02-14 , Earliest start: 2023-09-15 , Latest end: 2024-05-31

Organization Biomedical and Mobile Health Technology Lab

Hosts Ahmadizadeh Chakaveh

Topics Information, Computing and Communication Sciences , Engineering and Technology

Conduct human gait study with optical motion capture to assess smart clothing for movement monitoring

Biomedical and Mobile Health Technology Lab

We aim to conduct a study with human participants to assess the function of our textile-based wearable technology for movement monitoring in clinical and fitness scenarios.

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smart clothing, wearable technology, textile sensor, fitness tracking, sports medicine, rehabilitation, human study, motion capture, computer science

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Published since: 2024-02-14 , Earliest start: 2023-09-15 , Latest end: 2024-05-31

Organization Biomedical and Mobile Health Technology Lab

Hosts Ahmadizadeh Chakaveh

Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology

Imposition of non-fixed convex constraints on Neural Networks

Robotic Systems Lab

This project aims to answer the unsolved question of how to guarantee (in a computationally efficient way) hard convex constraints on the output of a network when the parameters that define the constraints change.

Keywords

Neural networks, convex, constraints, hard, optimization

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Semester Project , Collaboration , Master Thesis

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Published since: 2024-02-13 , Earliest start: 2024-02-13 , Latest end: 2025-03-29

Organization Robotic Systems Lab

Hosts Tordesillas Jesus

Topics Mathematical Sciences , Information, Computing and Communication Sciences

Controlling a Magnetically Actuated Inverted Pendulum

Multiscale Robotics Lab

Balancing a 3D inverted pendulum using remote magnetic actuation

Keywords

Inverted Pendulum, Machine Learning, Feedback Control, Dynamical Systems

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2024-02-08 , Earliest start: 2024-02-11 , Latest end: 2024-09-01

Organization Multiscale Robotics Lab

Hosts Zughaibi Jasan

Topics Engineering and Technology

Why do Parkinson's Patients walk differently?

Rehabilitation Engineering Lab

Investigation of brain activity during walking in Parkinson’s patients

Keywords

Parkinson's disease; EEG; neuroscience; biomechanics; movement;

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Semester Project , Internship , Lab Practice , Bachelor Thesis , Master Thesis

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Published since: 2024-02-02 , Earliest start: 2024-03-01 , Latest end: 2024-12-31

Organization Rehabilitation Engineering Lab

Hosts Salzmann Lena, MSc

Topics Medical and Health Sciences , Engineering and Technology

Probabilistic Air-ground Localization in Agricultural Environment

Vision for Robotics Lab

Digital environments, or digital twins, allow for design, prototyping, and testing in the virtual world before moving to the real world, thus accelerating development and reducing costs. Digital twin of a farm supports crop operations such as scheduling a harvest or predicting a yield, while agritech companies can develop farm automation robots using a digital twin. The goal of this project is to develop air-ground localization strategies that are capable to be deployed in crop environments during the production season. The main target is to identify individual plants in the ground images using as reference the aerial images.

Keywords

3d Reconstruction, Place Recognition, Aerial Vehicles, Deep Learning, Computer Vision.

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Semester Project , Master Thesis

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The construction of digital environments is increasingly important in industries like aerospace, automotive, and AEC. Digital environments, or digital twins, allow for design, prototyping, and testing in the virtual world before moving to the real world, thus accelerating development and reducing costs. Furthermore, a digital twin enables decision-making and -evaluation in the virtual world before committing to actions in the real world. Digital environments will play an equally important role for future agriculture. A digital twin of a farm supports crop operations such as scheduling a harvest or predicting a yield, while agritech companies can develop farm automation robots using a digital twin. An agricultural digital twin differs from an industrial digital twin in need for periodic monitoring of growing crops and conditions on farmland, and in requiring new methods for capturing vegetation geometry and modeling plant growth that exceed the state-of-the-art. One of the main challenges is the fusion of close-range ground imagery with aerial data. As such, the goal of this project is to develop air-ground localization strategies that are capable to be deployed in crop environments during the production season. The main target is to identify individual plants in the ground images using as reference the aerial images. For this, the dynamic aspects of the model will have to be taken into consideration. Geometric and Machine learning approaches will be necessary in this project. Simulation and real-drones will be used in the project. The student will have the opportunity to learn all aspects of the research, from the capture of the data to the final localization pipeline. Both perennial and non-perennial crops will be considered in the project. In addition, this work will be carried out in cooperation with an international company. The student taking this project needs to be highly motivated, preferably with strong analytical skills, while experience in coding in C/C++ and Python, Computer Vision, and Deep Learning would be very beneficial.

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Published since: 2024-02-02 , Earliest start: 2022-08-24 , Latest end: 2023-10-31

Organization Vision for Robotics Lab

Hosts Pinto Teixeira Lucas

Topics Information, Computing and Communication Sciences , Engineering and Technology

Long-Term Aerial Localization in Agricultural Environment

Autonomous Systems Lab

Digital environments, or digital twins, allow for design, prototyping, and testing in the virtual world before moving to the real world, thus accelerating development and reducing costs. A digital twin of a farm supports crop operations such as scheduling a harvest or predicting a yield, while agritech companies can develop farm automation robots using a digital twin. The goal of this project is to develop 3D Reconstruction and localization strategies that are capable to identify temporal invariant areas and properties in crop environments during the production season. The main target is to be able to match the same plants over time.

Keywords

3d Reconstruction, Place Recognition, Aerial Vehicles, Deep Learning, Computer Vision.

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Semester Project , Master Thesis

Description

The construction of digital environments is increasingly important in industries like aerospace, automotive, and AEC. Digital environments, or digital twins, allow for design, prototyping, and testing in the virtual world before moving to the real world, thus accelerating development and reducing costs. Furthermore, a digital twin enables decision-making and -evaluation in the virtual world before committing to actions in the real world. Digital environments will play an equally important role in future agriculture. A digital twin of a farm supports crop operations such as scheduling a harvest or predicting a yield, while agritech companies can develop farm automation robots using a digital twin. An agricultural digital twin differs from an industrial digital twin in the need for periodic monitoring of growing crops and conditions on farmland, and in requiring new methods for capturing vegetation geometry and modeling plant growth that exceed the state-of-the-art. As such, the goal of this project is to develop 3D Reconstruction and localization strategies that are capable to identify temporal invariant areas and properties in crop environments during the production season. The main target is to be able to match the same plants over time. For this, the dynamic aspects of the model will have to be taken in consideration. Geometric and Machine learning approaches will be necessary in this project. Simulation and real-drones will be used in the project. The student will have the opportunity to learn all aspect, since the capture of the data to the final temporal reconstruction. Both perennial and non-perennial crops will be considered in the project. In addition, this work will be carried out in cooperation with a international company. The student taking this project needs to be highly motivated, preferably with strong analytical skills, while experience in coding in C/C++ and Python, Computer Vision, and Deep Learning would be very beneficial. Main reference: Surber et al., Robust Visual-Inertial Localization with Weak GPS Priors for Repetitive UAV Flights, ICRA 2017

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Published since: 2024-02-02 , Earliest start: 2023-07-23 , Latest end: 2023-08-31

Organization Autonomous Systems Lab

Hosts Pinto Teixeira Lucas

Topics Information, Computing and Communication Sciences

Semantic Segmentation-Aided Bundle Adjustment

Autonomous Systems Lab

Bundle Adjustment (BA) is a critical optimization technique used to refine a visual reconstruction by jointly estimating the 3D scene structure and the viewing parameters. Traditional BA approaches primarily focus on geometric features and might struggle in highly unstructured scenarios, such as natural environments. This project aims to extend the Bundle Adjustment methodology by incorporating higher-level features extracted from semantic segmentation. The integration of semantic information aims to provide contextually relevant and more discriminative data to the adjustment process, thereby improving its accuracy and robustness.

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Semester Project , Master Thesis

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Published since: 2024-02-02 , Earliest start: 2023-07-30 , Latest end: 2024-05-01

Organization Autonomous Systems Lab

Hosts Pinto Teixeira Lucas , Mascaro Rubén

Topics Information, Computing and Communication Sciences

ANYmal Operator meets Mixed Reality

Robotic Systems Lab

This project aims to build an intuitive mixed reality (MR) interface for robot operators using Apple’s ARKit capabilities. Building on an existing iOS application, we want to provide the operators with a more immersive and informative interface. This involves showing the robot's sensory readings, sketching a global path for it, and playing back the robot's states in the application.

Keywords

ios, app, robotics, augmented reality

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Semester Project , Bachelor Thesis

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Published since: 2024-01-30 , Earliest start: 2024-02-01

Organization Robotic Systems Lab

Hosts Mittal Mayank

Topics Information, Computing and Communication Sciences

Microfluidic fabrication and acoustic control of gas filled liposomes

Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Already today, microbubbles are being used as ultrasound contrast agents. Their ability to precisely be manipulated to a target area gives rise to a lot of new possible applications. specially the ability to deliver drugs accurately and avoiding drug-tissue interactions with the surrounding healthy tissue would be groundbreaking for modern medicine. WE plan to implement a stable on-chip fabrication of liposomes and microbubbles and study the acoustic effect over the produced microbubbles. We investigate the effect of flow rates on the size of the produced bubbles and examine their stability.

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Acoustic, microrobots, drug delivery, biology, chemistry, biomedical, ultrasound, mechanical, microfluidics

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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2024-01-19

Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)

Hosts Del Alexia

Topics Engineering and Technology , Biology

Computational Modeling of Artificial Muscle Cells for Biohybrid Robots

Soft Robotics Lab

This research aims to advance biohybrid robotics by integrating living biological components with artificial materials. The focus is on developing computational models for artificial muscle cells, a critical element in creating biohybrid robots. Challenges include modeling the complex and nonlinear nature of biological muscles, considering factors like elasticity and muscle fatigue, as well as accounting for fluid-structure interaction in the artificial muscle's environment. The research combines first principle soft body simulation methods and machine learning to improve understanding and control of biohybrid systems.

Keywords

Biohybrid Robotics, Computational Models, Soft Body Simulation, Machine Learning, Surrogate Models, Finite Element Method (FEM)

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2024-01-05 , Earliest start: 2024-02-01 , Latest end: 2024-12-31

Organization Soft Robotics Lab

Hosts Katzschmann Robert, Prof. Dr. , Mekkattu Manuel

Topics Information, Computing and Communication Sciences , Engineering and Technology , Biology , Physics

DELTA

Rehabilitation Engineering Lab

This project investigates the possibility to use low budget sensors such as webcams and IMUs to measure movement of stroke patients and quantify the movement quality. This low cost approach will allow to scale the solutions and bring instrumented solutions into clinical application. Integral part of this project is to develop and validate algorithms, create user-friendly apps and translate the new technology into clinical application. This project is a collaboration between ETH and cereneo foundation and is thus based in Zurich and Vitznau/Hertenstein.

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AI, markerless motioncapture, stroke, assessments, computer vision, low budget, IMU

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Semester Project , Course Project , Internship , Bachelor Thesis , Master Thesis

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Published since: 2023-12-13 , Earliest start: 2023-12-18

Organization Rehabilitation Engineering Lab

Hosts Unger Tim

Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences

3D reconstruction with event cameras

Robotics and Perception

This project will explore the application of event camera setups for scene reconstruction. Accurate and efficient reconstructions using event-camera setups is still an unexplored topic. This project will focus on solving the problem of 3D reconstruction using active perception with event cameras.

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Event camera, computer vision, Depth, 3D reconstruction

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Semester Project , Master Thesis

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Published since: 2023-12-11 , Earliest start: 2022-02-15

Applications limited to University of Zurich , ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne

Organization Robotics and Perception

Hosts Muglikar Manasi

Topics Information, Computing and Communication Sciences

Model-based Reinforcement Learning for Autonomous Drone Racing

Robotics and Perception

Recent advances in model-free Reinforcement Learning have shown superior performance in different complex tasks, such as the game of chess, quadrupedal locomotion, or even drone racing. Given a reward function, Reinforcement Learning is able to find the optimal policy through trial-and-error in simulation, that can directly be deployed in the real-world. In our lab, we have been able to outrace professional human pilots using model-free Reinforcement Learning trained solely in simulation.

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Semester Project , Master Thesis

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Published since: 2023-12-11 , Earliest start: 2023-01-31 , Latest end: 2024-08-01

Organization Robotics and Perception

Hosts Romero Angel

Topics Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences

Anomaly Detection in Excavation Using Machine Learning

Robotic Systems Lab

This project aims to develop an algorithm for detecting anomalies during autonomous excavation. The goal is to enable these machines to identify obstacles like rocks, buried cables, or pipes to prevent damage or delays. Key challenges include creating realistic simulations and achieving a low false positive rate in various soil environments. The project will involve both simulation and hardware testing on full-sized excavators.

Keywords

Machine learning, sim-to-real, tactile sensing, autonomous robots, autonomous excavator

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Semester Project , Master Thesis

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Published since: 2023-12-06 , Earliest start: 2024-02-29 , Latest end: 2024-06-30

Organization Robotic Systems Lab

Hosts Zhang Weixuan

Topics Information, Computing and Communication Sciences , Engineering and Technology

Learning State Estimation for Locomotion

Robotic Systems Lab

Model-based state estimation for locomotion has shown some significant drawbacks, especially in the case of complex contact scenarios. At the same time, locomotion controllers are evolving, now purposely using knee contacts or wheel slippage for advanced motions. The current model-based state estimation techniques often cannot supply sufficiently accurate observations for these controllers, leading to major estimation drifts and thus potential failures. In this project, we aim to leverage learning-based methods not only for locomotion control, but also for state estimation. Preliminary work shows that creating a state estimation through supervised learning from recorded simulation data can produce a viable solution. Furthermore, fusing these approaches with classical filtering theory opens a promising realm of research. The project should also compare the developed methods with existing approaches on real hardware. If time permits, we are interested in learning state estimation and locomotion jointly.

Keywords

State Estimation Machine Learning Legged Locomotion

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Semester Project , Master Thesis

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Published since: 2023-12-01

Organization Robotic Systems Lab

Hosts Klemm Victor , Rudin Nikita

Topics Information, Computing and Communication Sciences , Engineering and Technology

Pick-up and Loading of Packages by Transformable Quadrupeds

Robotic Systems Lab

Transport of packages of various dimensions is often mentioned as one of the most viable use cases for autonomous mobile robots. The ability to autonomously pick up and self-load a package is, however, a functionality that many systems are still lacking. Preliminary work showed that quadrupedal robots have the potential to execute this skill by manipulating payloads with their legs or main body. In this project, we aim to investigate how legged and wheeled legged robots can achieve autonomous package pick-and-load tasks with practical design modifications and clever maneuvers.

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Robot Design Payload Manipulation Gripper Mechanism Transformable Quadruped

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Semester Project , Master Thesis

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Published since: 2023-12-01

Organization Robotic Systems Lab

Hosts Klemm Victor , Baines Robert

Topics Information, Computing and Communication Sciences , Engineering and Technology

Label transfer from satellite to aerial imagery

Autonomous Systems Lab

The project aims to implement a semantic label transfer from satellite to aerial imagery in order to enable the training of image-based machine learning algorithms for autonomous aerial vehicle tasks, such as path planning, collision avoidance, and localization.

Keywords

semantic scene information, dataset generation, air-satellite semantic label transfer, vision-based deep learning, computer vision, drone navigation

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Published since: 2023-11-30 , Earliest start: 2023-03-23 , Latest end: 2023-04-30

Organization Autonomous Systems Lab

Hosts Mascaro Rubén , Pinto Teixeira Lucas

Topics Information, Computing and Communication Sciences

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