Projects and Theses
Design and development of a novel printing approach
3D printing has revolutionized the way objects are designed and fabricated across a wide range of industries—from aerospace and automotive to healthcare and consumer products. It enables rapid prototyping, complex geometries, customized solutions, and recently bioprinting of living tissues that are difficult or impossible to achieve with traditional manufacturing methods. Every 3D printing method has certain drawbacks, often related to resolution, material compatibility, speed, or scalability. The ongoing search for new approaches aims to overcome these challenges and expand the potential of the technology. We have developed and demonstrated a proof of concept for a novel printing approach, and are now seeking to advance it into a fully functional prototype.
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Semester Project , Master Thesis
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Published since: 2025-05-15 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Zenobi-Wong Group / Tissue Engineering and Biofabrication
Hosts Janiak Jakub
Topics Engineering and Technology
Parkour With Boston Dynamics' Robot Spot (RAI collaboration)
ANYmal has demonstrated almost animal-like agility and confidence on several parkour elements, utilizing jumping, crouching and climbing behaviors. In this master thesis we aim to extend our training pipeline for synthesizing such specialized motions to new robots, in particular, Boston Dynamic's quadruped Spot. We speculate that the increased joint velocity limits and the longer legs may lead to even higher performance, greater success rate, and more diverse parkour skills.
Keywords
Reinforcement Learning, RL, Dagger, policy distillation
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Master Thesis
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Published since: 2025-05-14 , Earliest start: 2025-05-14
Organization Robotic Systems Lab
Hosts Jenelten Fabian , Schwarke Clemens
Topics Information, Computing and Communication Sciences
Stanford – UC Berkeley Collaboration: Learning Progress Driven Reinforcement Learning for ANYmal
TLDR: Improving navigation capabilities of ANYmal - RL is simulation - optimizing learning progress.
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Semester Project , Master Thesis
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Published since: 2025-05-14 , Earliest start: 2025-05-14 , Latest end: 2025-08-31
Applications limited to EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , University of Zurich
Organization Robotic Systems Lab
Hosts Frey Jonas
Topics Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences
Multi-Critic Reinforcement Learning for Whole-Body Control of Bimanual Legged Manipulator
Recent work in legged robotics shows the promise of unified control strategies for whole-body control. Portela et al. (2024) demonstrated force control without force sensors, enabling compliant manipulation through body coordination. In another study, they achieved accurate end-effector tracking using whole-body RL with terrain-aware sampling. Fu et al. (2023) showed that unified policies can dynamically handle both movement and manipulation in quadruped robots by training with two critics: one for arms, and one for legs, and then gradually combining them. In this project, you will investigate reinforcement learning for whole body control of a bimanual legged manipulator. You will implement a baseline single-critic whole body controller for the system. You will then investigate different multi-critic approaches and their effects on the training and final performance of the whole-body controller. References: Learning Force Control for Legged Manipulation, Portela et al., 2024 Whole-Body End-Effector Pose Tracking, Portela et al., 2024 Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion, Fu et al., 2023
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Semester Project , Master Thesis
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Published since: 2025-05-09 , Earliest start: 2025-05-11 , Latest end: 2025-12-31
Applications limited to ETH Zurich , [nothing]
Organization Robotic Systems Lab
Hosts Fischer Oliver , Elanjimattathil Aravind
Topics Engineering and Technology
Diffusing Time Series in the Wavelet Domain
Diffusion models (DDPMs) have revolutionised generative modelling, surpassing GANs in images, advancing audio synthesis, and enabling de-novo protein design. Yet progress on time series lags behind early adversarial work. Recent studies highlight the benefits of spectral biases - FourierFlow and frequency-domain DDPMs. In parallel, diffusion in the wavelet domain has emerged for images, offering a multi-resolution view well-suited to non-stationary signals. Wavelets capture localised, scale-dependent features, making them attractive for domains from finance to climate and biomedical data such as ECGs. This project proposes the first DDPM framework operating directly in the wavelet domain for time series, aiming to improve generalisation, interpretability, and robustness across diverse sequential tasks.
Keywords
Diffusion Models, Time Series, Wavelet Domain
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-05-09 , Earliest start: 2025-05-09 , Latest end: 2025-12-30
Organization Medical Data Science
Hosts Ruiperez-Campillo Samuel
Topics Mathematical Sciences , Information, Computing and Communication Sciences
Visual Language Models for Long-Term Planning
This project uses Visual Language Models (VLMs) for high-level planning and supervision in construction tasks, enabling task prioritization, dynamic adaptation, and multi-robot collaboration for excavation and site management. prioritization, dynamic adaptation, and multi-robot collaboration for excavation and site management
Keywords
Visual Language Models, Long-term planning, Robotics
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Semester Project , Master Thesis
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Published since: 2025-05-07 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
AI Agents for Excavation Planning
Recent advancements in AI, particularly with models like Claude 3.7 Sonnet, have showcased enhanced reasoning capabilities. This project aims to harness such models for excavation planning tasks, drawing parallels from complex automation scenarios in games like Factorio. We will explore the potential of these AI agents to plan and optimize excavation processes, transitioning from simulated environments to real-world applications with our excavator robot.
Keywords
GPT, Large Language Models, Robotics, Deep Learning, Reinforcement Learning
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Semester Project , Master Thesis
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Published since: 2025-05-07 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Engineering and Technology
Flexible Wireless Sensing Node for Continuous Body Monitoring
Advancements in sensor technology, low-power mixed-signal/RF circuits, and Wireless Sensor Networks (WSNs) have enabled the creation of compact, cost-effective solutions for healthcare applications. A notable development in this field is the Body Sensor Network, which is designed to monitor the human body for healthcare purposes.
Keywords
Embedded systems, PCB design, Firmware development, Data Analysis
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Semester Project , Internship , Bachelor Thesis
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Published since: 2025-04-30 , Earliest start: 2025-01-12
Organization Digital Circuits and Systems (Benini)
Hosts Spacone Giusy
Topics Engineering and Technology
BEV meets Semantic traversability
Enable Birds-Eye-View perception on autonomous mobile robots for human-like navigation.
Keywords
Semantic Traversability, Birds-Eye-View, Localization, SLAM, Object Detection
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-04-29 , Earliest start: 2025-01-15 , Latest end: 2025-10-31
Organization Robotic Systems Lab
Hosts Gawel Abel
Topics Information, Computing and Communication Sciences , Engineering and Technology
Scene graphs for robot navigation and reasoning
Elevate semantic scene graphs to a new level and perform semantically-guided navigation and interaction with real robots at The AI Institute.
Keywords
Scene graphs, SLAM, Navigation, Spacial Reasoning, 3D reconstruction, Semantics
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-04-29 , Earliest start: 2025-01-15 , Latest end: 2025-10-31
Organization Robotic Systems Lab
Hosts Gawel Abel
Topics Information, Computing and Communication Sciences , Engineering and Technology
Modelling and Optimizing the Power Budget of a Bridge-Mounted Camera System for River Waste Monitoring
In this thesis, you will contribute to the Autonomous River Cleanup (ARC) by helping develop SARA, a bridge-mounted, camera-based system for monitoring river waste. Your focus will be on modeling the system’s power dynamics to determine the ideal battery and solar panel size, and balancing runtime throughout the day with overall the system size and weight. If time allows, you will also validate your findings with tests on the real hardware.
Keywords
system modelling, power electronics, simulations
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Semester Project , Bachelor Thesis
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Published since: 2025-04-27 , Earliest start: 2025-05-05 , Latest end: 2025-09-30
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Elbir Emre
Topics Engineering and Technology
Domain Adaptation Techniques for Vision Algorithms on a Smartphone for River Waste Monitoring
In this thesis, you will work on SARA, a bridge-mounted, smartphone-based system for detecting and monitoring river waste. The focus will be on selecting lightweight detection and classification models suitable for smartphones and exploring domain adaptation techniques to improve performance across different locations with minimal retraining. Your work will build on previous research at ARC and current literature to develop solutions that balance model robustness and computational efficiency.
Keywords
machine learning, computer vision, domain adaptation techniques
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Published since: 2025-04-27 , Earliest start: 2025-05-05 , Latest end: 2025-09-30
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Elbir Emre
Topics Engineering and Technology
Optimal Robot Configuration for Autonomous Waste Sorting in Confined Spaces
In this thesis, you will contribute to the Autonomous River Cleanup (ARC) by helping improve MARC, our robotic platform for autonomous waste sorting. Your work will focus on optimizing the robot arm configuration by simulating different base locations and degrees of freedom to achieve faster and more efficient pick-and-place movements in a confined space. You will build on our existing simulation environment to model and evaluate various setups.
Keywords
modelling and simulations, robotics, robot dynamics
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Published since: 2025-04-27 , Earliest start: 2025-05-05 , Latest end: 2025-09-30
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Organization Robotic Systems Lab
Hosts Elbir Emre
Topics Engineering and Technology
Thermal Protection of a Bridge-Mounted Camera System for River Waste Monitoring
The Autonomous River Cleanup (ARC) is developing SARA, the next iteration of a bridge-mounted, camera-based system to detect and measure riverine waste. Smartphones offer a compact, affordable, and powerful core for year-round monitoring but are vulnerable to shutdowns from extreme heat in summer and cold in winter. This thesis focuses on assessing these thermal challenges and designing protective solutions to ensure reliable, continuous operation.
Keywords
thermodynamics, heat transfer, testing
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Semester Project , Bachelor Thesis
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Published since: 2025-04-27 , Earliest start: 2025-05-05 , Latest end: 2025-09-30
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Organization Robotic Systems Lab
Hosts Elbir Emre
Topics Engineering and Technology
Utilizing the human body for ambient electromagnetic energy harvesting
The goal of the project is to develop wearable devices, for use in environmental electromagnetic energy recovery based on human body application.
Keywords
Flexible electronics, electromagnetic energy harvesting
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-04-23 , Earliest start: 2025-06-20
Organization Biomedical and Mobile Health Technology Lab
Hosts Li Yuanlong
Topics Engineering and Technology
Gaussian Avatar Reconstruction from Single Image
In this project, you are going to work with a state-of-the-art deep learning approach and generative models for building an efficient system to directly reconstruct a 3D animatable avatar from a single image. Feel free to contact me for more details.
Keywords
3D Gaussian Avatar, Diffusion Model, 3D from single image
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-04-23 , Earliest start: 2025-05-01 , Latest end: 2025-12-15
Organization Advanced Interactive Technologies
Hosts Dong Zijian
Topics Information, Computing and Communication Sciences
Mechanophores for advanced wearable strain and pressure sensors
The goal of the project is to synthesize and characterize a number of small molecules capable of acting as mechanophore addition to various polymers. These polymers would then be used as wearable strain or pressure sensors.
Keywords
mechanophore, polymer, wearable, sensor, color, strain, pressure
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Master Thesis
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Published since: 2025-04-22 , Earliest start: 2024-09-01 , Latest end: 2025-09-01
Organization Biomedical and Mobile Health Technology Lab
Hosts Shokurov Aleksandr
Topics Engineering and Technology , Chemistry
Point-of-Care Sensor for Urinary Iodine
The goal of the project is to develop a cheap and disposable sensor capable of determination of iodine levels in human urine for early diagnostic purposes.
Keywords
electrochemistry, iodine, nutrition, health, point of care
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Master Thesis
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Published since: 2025-04-22 , Earliest start: 2025-01-01 , Latest end: 2025-10-01
Organization Biomedical and Mobile Health Technology Lab
Hosts Shokurov Aleksandr
Topics Medical and Health Sciences , Engineering and Technology , Chemistry
Tissue Engineering Approaches to Study Tendon Injury, Disease, and Therapy
Join a dynamic research team at the intersection of biomechanics, tissue engineering, and cell biology. This project offers hands-on training in state-of-the-art methods to investigate how tendon tissue responds to injury, disease processes, and mechanical stimulation during exercise-based therapy.
Keywords
Tendon biology, tissue engineering, mechanobiology, cell culture, microscopy, regenerative medicine, exercise therapy, inflammation, ECM remodeling
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Master Thesis
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Published since: 2025-04-15 , Earliest start: 2025-05-01 , Latest end: 2026-12-31
Organization Snedeker Group / Laboratory for Orthopaedic Biomechanics
Hosts Snedeker Jess, Prof.
Topics Engineering and Technology
Experimental and Numerical Investigation of Direction-Dependent Flow Resistance in Engineered Geometries
Controlling fluid flow is essential in various natural and engineering systems, with geometry playing a fundamental role in shaping fluid behavior. However, the interaction between geometry and flow behavior remains a complex phenomenon, primarily governed by the flow regime and fluid material properties. Certain geometries, whether naturally occurring or engineered, induce direction-dependent flow resistance, causing variations in velocity and flow rate in opposite directions. One well-known example of such engineered geometries is the Tesla valve—a passive device without moving parts, designed to create asymmetric flow resistance, particularly at high Reynolds numbers. This structure acts like a fluidic diode, offering greater resistance to flow in one direction by generating turbulent vortices and flow separations while allowing smoother movement in the opposite direction. This effect is quantified by diodicity, which represents the ratio of pressure drop in the reverse direction to that in the forward direction, providing a measure of the valve's asymmetric resistance. However, this direction dependence is limited at lower velocities. We have designed two sets of geometries that effectively induce directional flow resistance within high and low fluid flow velocities. This Master’s thesis project aims to experimentally investigate the impact of different flow obstruction designs on direction-dependent resistance in rectangular channels and semicircular arc segments. The student will, together with their direct supervisor, design and construct an experimental setup for the reliable measurement of flow and diodicity. This project offers an excellent opportunity to gain expertise in fluid dynamics, experimental testing, numerical modeling, and additive manufacturing, with applications in biomedical systems. Students with a background in mechanical engineering, fluid dynamics, or related fields are encouraged to apply. Prior experience with COMSOL Multiphysics is beneficial but not mandatory.
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Master Thesis
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Published since: 2025-04-15 , Earliest start: 2025-06-01 , Latest end: 2025-12-01
Organization Musculoskeletal Biomechanics
Hosts Mosayebi Mahdieh
Topics Engineering and Technology
Research Assistant with data collection,cleaning,processing and programming skills
We are looking for a research assistant who is skilled at data collection, cleaning, matching and programming. Please see details in the attachment.
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Student Assistant / HiWi
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Published since: 2025-04-13 , Earliest start: 2025-04-13 , Latest end: 2025-05-15
Organization Chair of Strategic Management and Innovation
Hosts Liu Chang
Topics Information, Computing and Communication Sciences
Hardware Design Internship in Brain Imaging
Join us in revolutionizing brain imaging technologies and make it accessible for everyday use. Functional near-infrared spectroscopy (fNIRS) is an emerging technology that enables cost-effective and precise brain measurements, helping to improve neurotherapies and brain health.
Keywords
3D-printing, injection molding, design, brain imaging, neuro, wearables, health, startup
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Internship
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Published since: 2025-04-09 , Earliest start: 2025-04-10 , Latest end: 2025-06-26
Organization Rehabilitation Engineering Lab
Hosts Wyser Dominik
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
PCB design for neuromorphic vision on nano-drones
In this project, we aim to develop a novel PCB integrating a powerful PULP chip, i.e., the GAP9, and event-based sensor, the Prophesee Genx320, and a RGB camera, the Himax HB0360 to enable multi modal AI-driven perception aboard nano-drones
Keywords
PCB design, nano-drones, robotics, event-cameras, neuromorphic computing, embedded devices, ultra-low-power
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-04-07 , Earliest start: 2025-04-07 , Latest end: 2025-12-01
Organization Digital Circuits and Systems (Benini)
Hosts Lamberti Lorenzo
Topics Engineering and Technology
Continual Learning and Domain Adaptation Techniques for a Camera-Based Waste Monitoring System on an Ocean Cleanup Vessel
This thesis, part of the Autonomous River Cleanup (ARC) initiative in collaboration with The SeaCleaners, explores adaptive computer vision methods for automated quantification of oceanic plastic waste on the Mobula 10 vessel. The work focuses on applying continual learning and domain adaptation techniques to improve a baseline detection model’s robustness to changing waste types and environments. The system will be evaluated in real-world conditions to assess its performance and guide future research in environmental monitoring.
Keywords
computer vision, continual learning, field testing, unsupervised domain adaptation, plastic pollution
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Master Thesis
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Published since: 2025-03-26 , Earliest start: 2025-05-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Stolle Jonas , Elbir Emre
Topics Engineering and Technology
Master Thesis / Project - SENSEI: Sensor Teaching in Multi-Activity classification from Video and Wearables for Wheelchair Users
In this project, we focus on continuous and quantitative monitoring of activities of daily living (ADL) in SCI individuals with the goal of identifying cardiovascular events and PI-related risk behaviors. ADLs specific to SCI patients and their lifestyles shall be discussed and narrowed down in the scope of this work, therefore an autonomous camera-based system is proposed to classify ADLs. The Current work builds on a previous project where a SlowFast network [1] was trained to identify SCI-specific classes and we aim to further improve the classification and temporal resolution for transferring to wearables' time-series data.
Keywords
Computer vision, activity classification, video processing, Deep Learning, ADL, soft-labelling, probabilistic networks
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Semester Project , Course Project , Internship , Bachelor Thesis , Master Thesis , ETH for Development (ETH4D) (ETHZ) , ETH Zurich (ETHZ)
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Published since: 2025-03-25 , Earliest start: 2025-05-01 , Latest end: 2026-02-28
Applications limited to EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , Zurich University of the Arts , Wyss Translational Center Zurich , University of Zurich , Zurich University of Applied Sciences , CERN , CSEM - Centre Suisse d'Electronique et Microtechnique , Department of Quantitative Biomedicine , Lucerne University of Applied Sciences and Arts , Institute for Research in Biomedicine , IBM Research Zurich Lab , University of St. Gallen , University of Lucerne , University of Lausanne , University of Geneva , University of Fribourg , University of Berne , University of Basel , Université de Neuchâtel , Swiss Institute of Bioinformatics , Swiss National Science Foundation , Swiss Federal Institute for Forest, Snow and Landscape Research , Institute of Robotics and Intelligent Systems D-MAVT , TU Berlin , TU Darmstadt , TU Dresden , RWTH Aachen University , Technische Universität München , Technische Universität Hamburg , Max Planck Society , University of Oxford , University of Leeds , University of Cambridge , UCL - University College London , National Institute for Medical Research , Imperial College London , Royal College of Art , Empa , Università della Svizzera italiana , Hochschulmedizin Zürich , Hong Kong University of Science and Technology , University of Washington , Tokyo Institute of Technology , The University of Tokyo
Organization Sensory-Motor Systems Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Behavioural and Cognitive Sciences
Extending Functional Scene Graphs to Include Articulated Object States
While traditional [1] and functional [2] scene graphs are capable of capturing the spatial relationships and functional interactions between objects and spaces, they encode each object as static, with fixed geometry. In this project, we aim to enable the estimation of the state of articulated objects and include it in the functional scene graph.
Keywords
scene understanding, scene graph, exploration
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Master Thesis
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Published since: 2025-03-25 , Earliest start: 2025-03-25
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Computer Vision and Geometry Group
Hosts Bauer Zuria, Dr. , Trisovic Jelena , Zurbrügg René
Topics Information, Computing and Communication Sciences , Engineering and Technology
Master Thesis: Development of a Customized Knee Orthosis for Osteoarthritis
Osteoarthritis (OA) presents a significant challenge in healthcare, necessitating innovative solutions to alleviate pain, enhance mobility. This thesis documents the research and development journey of an OA knee orthosis within the Spinal Cord and Artificial Intelligence Lab (SCAI-Lab) at ETH Zurich. This thesis is a close collaboration between the ORTHO-TEAM Group and the SCAI-Lab at ETH Zurich. The collaboration offers a unique exchange of expertise and resources between industry and academia. Together, we aim to make meaningful progress in the field of and empower students to make valuable contributions to their academic pursuits.
Keywords
Osteo Arthritis, Orthosis, Biomechanics, AI, Medical Data, Healthcare
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-03-25 , Earliest start: 2025-04-15 , Latest end: 2026-01-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa , University of Basel , University of Berne , Zurich University of Applied Sciences , Università della Svizzera italiana , Hochschulmedizin Zürich , Lucerne University of Applied Sciences and Arts , Institute for Research in Biomedicine , CSEM - Centre Suisse d'Electronique et Microtechnique
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Engineering and Technology
Development of a Heterocellular Human Bone Organoid for Precision Medicine and Treatment
Our goal is to establish a heterocellular 3D printed bone organoid model comprising all major bone cell types (osteoblasts, osteocytes, osteoclasts) to recapitulate bone remodeling units in an in vitro system. The organoids will be produced with the human cells, as they could represent human pathophysiology better than animal models, and eventually could replace them. These in vitro models could be used in the advancement of next-generation personalised treatment strategies. Our tools are different kinds of 3D bioprinting platforms, bio-ink formulations, hydrogels, mol-bioassays, and time-lapsed image processing of micro-CT scans.
Keywords
3D printing, bone organoids, co-culture, bioreactor, hydrogels, drug testing
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Semester Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-03-24 , Earliest start: 2022-08-01 , Latest end: 2025-11-30
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Steffi Chris
Topics Engineering and Technology , Biology
Exploring the Mechanoregulation of Bone Regeneration
In over 100 years, the remarkable ability of bone to adapt to its mechanical environment has been a source of scientific fascination. Bone regeneration has been shown to be highly dependent on the mechanical environment at the fracture site. It has been demonstrated that mechanical stimuli can either accelerate or impede regeneration. Despite the fundamental importance of the mechanical environment in influencing bone regeneration, the molecular mechanisms underlying this phenomenon are complex and poorly understood.
Keywords
Bone, Mechanobiology, Spatial transcriptomics, Gene expression, Finite element modelling, Image processing
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2025-03-23 , Earliest start: 2024-11-01 , Latest end: 2025-08-31
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Mathavan Neashan
Topics Medical and Health Sciences , Engineering and Technology
Event-based feature detection for highly dynamic tracking
Event cameras are an exciting new technology enabling sensing of highly dynamic content over a broad range of illumination conditions. The present thesis explores novel, sparse, event-driven paradigms for detecting structure and motion patterns in raw event streams.
Keywords
Event camera, neuromorphic sensing, feature detection, computer vision
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Master Thesis
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Published since: 2025-03-13 , Earliest start: 2025-03-17
Organization Robotic Systems Lab
Hosts Kneip Laurent
Topics Engineering and Technology
Fast, change-aware map-based camera tracking
Experiment with Gaussian Splatting based map representations for highly efficient camera tracking and simultaneous change detection and map updating. Apply to different exteroceptive sensing modalities.
Keywords
Localization, Camera Tracking, Gaussian Splatting, Change detection
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Master Thesis
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Published since: 2025-03-13 , Earliest start: 2025-03-17
Organization Robotic Systems Lab
Hosts Kneip Laurent
Topics Engineering and Technology
Soft object reconstruction
This project consists of reconstructing soft object along with their appearance, geometry, and physical properties from image data for inclusion in reinforcement learning frameworks for manipulation tasks.
Keywords
Computer Vision, Structure from Motion, Image-based Reconstruction, Physics-based Reconstruction
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Master Thesis
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Published since: 2025-03-13 , Earliest start: 2025-03-17
Organization Robotic Systems Lab
Hosts Kneip Laurent
Topics Engineering and Technology
Reconstruction from online videos taken in the wild
Push the limits of arbitrary online video reconstruction by combining the most recent, prior-supported real-time Simultaneous Localization And Mapping (SLAM) methods with automatic supervision techniques.
Keywords
Computer Vision, 3D Reconstruction, SLAM
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Master Thesis
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Published since: 2025-03-13 , Earliest start: 2025-03-17
Organization Robotic Systems Lab
Hosts Kneip Laurent
Topics Engineering and Technology
Computationally Efficient Neural Networks
Computing, time, and energy requirements of recent neural networks have demonstrated dramatic increase over time, impacting on their applicability in real-world contexts. The present thesis explores novel ways of implementing neural network implementations that will substantially reduce their computational complexity and thus energy footprint.
Keywords
AI, CNNs, transformers, network implementation
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Master Thesis
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Published since: 2025-03-12 , Earliest start: 2025-03-17
Organization Robotic Systems Lab
Hosts Kneip Laurent
Topics Engineering and Technology
Generalist Excavator Transformer
We want to develop a generalist digging agent that is able to do multiple tasks, such as digging and moving loose soil, and/or control multiple excavators. We plan to use decision transformers, trained on offline data, to accomplish these tasks.
Keywords
Offline reinforcement learning, transformers, autonomous excavation
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Semester Project , Master Thesis
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Published since: 2025-03-11 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Werner Lennart , Egli Pascal Arturo , Terenzi Lorenzo , Nan Fang , Zhang Weixuan
Topics Information, Computing and Communication Sciences
Exploring the 3D Mineralization Behavior in Material-Induced Osteoinduction Through a Multiscale Micro-CT Imaging Approach
The project aims at investigating material-induced osteoinduction using the available mouse model of orthotopic or ectopic bone graft substitute (BGS) application. Through the 3D-3D registration of ex vivo and in vivo multiscale micro-CT images, crucial 3D mineralization behavior of the BGS can be investigated.
Keywords
Femur, Bone Graft Substitute, Critical Size Defect, Osteoinduction, in vivo, micro-CT, 3D-3D Image Registration, Image Analysis, Image Processing, Python, Computational
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-03-11 , Earliest start: 2025-04-01 , Latest end: 2026-01-31
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Lindenmann Sara
Topics Medical and Health Sciences , Engineering and Technology
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: 2025-03-10 , 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
Wearable kirigami antenna for motion monitoring
The aim of the project is to develop a simple method for fabrication of kirigami-inspired laser-cut or molded antennas on flexible substrates. This technology will enable advancements in wearable electronics for wireless communication and sensing applications.
Keywords
wearable, flexible electronics, kirigami, laser cutting, 3D printing, antenna design, conductivity, wireless communication
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Semester Project , Bachelor Thesis
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Published since: 2025-03-09 , Earliest start: 2025-03-24 , Latest end: 2026-08-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Kateb Pierre
Topics Engineering and Technology
Master Thesis: Contact force evaluation of robotic endoscopic system based on Series Elastic Actuation
In the BIROMED-Lab we have been developing an endoscopic system for safer neurosurgeries with inspiration from human finger anatomy. Its two degrees of freedom allow the endoscope to investigate areas of the brain that would be inaccessible with standard rigid endoscopes. Thanks to springs in the transmission between the motors and the movable endoscope tip, the interaction forces between the instrument and the brain tissue can be reduced. Furthermore the interaction forces can be estimated by measuring the deflection of the spring. To make the telemanipulation of the endoscope safer and more intuitive for the surgeon, force feedback was also implemented.
Keywords
Robotic surgery, Neurosurgery, Telemanipulation, Haptic feedback, Robotic endoscope
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Master Thesis
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Published since: 2025-03-06 , Earliest start: 2025-03-01
Organization Bio-Inspired RObots for MEDicine-Laboratory (BIROMED-Lab)
Hosts Ettori Sara Lisa Margherita , Gerig Nicolas, Dr. , Sommerhalder Michael
Topics Engineering and Technology
Novel Winch Control for Robotic Climbing
While legged robots have demonstrated impressive locomotion performance in structured environments, challenges persist in navigating steep natural terrain and loose, granular soil. These challenges extend to extraterrestrial environments and are relevant to future lunar, martian, and asteroidal missions. In order to explore the most extreme terrains, a novel winch system has been developed for the ANYmal robot platform. The winch could potentially be used as a fail-safe device to prevent falls during unassisted traverses of steep terrain, as well as an added driven degree of freedom for assisted ascending and descending of terrain too steep for unassisted traversal. The goal of this project is to develop control policies that utilize this new hardware and enable further climbing robot research.
Keywords
Robot, Space, Climbing, Winch, Control
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-03-05 , Earliest start: 2024-10-07
Organization Robotic Systems Lab
Hosts Vogel Dylan
Topics Information, Computing and Communication Sciences , Engineering and Technology
Beyond Value Functions: Stable Robot Learning with Monte-Carlo GRPO
Robotics is dominated by on-policy reinforcement learning: the paradigm of training a robot controller by iteratively interacting with the environment and maximizing some objective. A crucial idea to make this work is the Advantage Function. On each policy update, algorithms typically sum up the gradient log probabilities of all actions taken in the robot simulation. The advantage function increases or decreases the probabilities of these taken actions by comparing their “goodness” versus a baseline. Current advantage estimation methods use a value function to aggregate robot experience and hence decrease variance. This improves sample efficiency at the cost of introducing some bias. Stably training large language models via reinforcement learning is well-known to be a challenging task. A line of recent work [1, 2] has used Group-Relative Policy Optimization (GRPO) to achieve this feat. In GRPO, a series of answers are generated for each query-answer pair. The advantage is calculated based on a given answer being better than the average answer to the query. In this formulation, no value function is required. Can we adapt GRPO towards robot learning? Value Functions are known to cause issues in training stability [3] and a result in biased advantage estimates [4]. We are in the age of GPU-accelerated RL [5], training policies by simulating thousands of robot instances simultaneously. This makes a new monte-carlo (MC) approach towards RL timely, feasible and appealing. In this project, the student will be tasked to investigate the limitations of value-function based advantage estimation. Using GRPO as a starting point, the student will then develop MC-based algorithms that use the GPU’s parallel simulation capabilities for stable RL training for unbiased variance reduction while maintaining a competitive wall-clock time.
Keywords
Robot Learning, Reinforcement Learning, Monte Carlo RL, GRPO, Advantage Estimation
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-03-05
Organization Robotic Systems Lab
Hosts Klemm Victor
Topics Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences
Volumetric Bucket-Fill Estimation
Gravis Robotics is an ETH spinoff from the Robotic Systems Lab (RSL) working on the automation of heavy machinery (https://gravisrobotics.com/). In this project, you will be working with the Gravis team to develop a perceptive bucket-fill estimation system. You will conduct your project at Gravis under joint supervision from RSL.
Keywords
Autonomous Excavation
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Semester Project , Master Thesis
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Published since: 2025-02-28 , Earliest start: 2025-01-01 , Latest end: 2026-01-01
Organization Robotic Systems Lab
Hosts Egli Pascal Arturo
Topics Engineering and Technology
Master Thesis: Vibro-tactile feedback in ventricle puncturing during External Ventricular Drain (EVD) procedure
EVD is a common procedure in Neurosurgery, nevertheless its placement is non-ideal in up to 40% of the cases because of lack of hands-on experience of residents. To try and solve the issue we propose a medical simulator that will merge haptic feedback with hardware components. Vibro-tactile feedback has been proven useful in medical simulations and could give a more complete and realistic experience to the training surgeon, either as supplementary information to the force feedback or as stand alone information. In order to feed back the vibro-tactile information to the user, the haptic device has to be instrumentalized with appropriate custom-made hardware.
Keywords
Vibro-tactile feedback, Haptic feedback, Medical robotics, Surgical simulators
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Master Thesis
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Published since: 2025-02-26 , Earliest start: 2025-03-01
Organization Bio-Inspired RObots for MEDicine-Laboratory (BIROMED-Lab)
Hosts Gerig Nicolas, Dr. , Sommerhalder Michael , Ettori Sara Lisa Margherita
Topics Engineering and Technology
Leveraging Human Motion Data from Videos for Humanoid Robot Motion Learning
The advancement in humanoid robotics has reached a stage where mimicking complex human motions with high accuracy is crucial for tasks ranging from entertainment to human-robot interaction in dynamic environments. Traditional approaches in motion learning, particularly for humanoid robots, rely heavily on motion capture (MoCap) data. However, acquiring large amounts of high-quality MoCap data is both expensive and logistically challenging. In contrast, video footage of human activities, such as sports events or dance performances, is widely available and offers an abundant source of motion data. Building on recent advancements in extracting and utilizing human motion from videos, such as the method proposed in WHAM (refer to the paper "Learning Physically Simulated Tennis Skills from Broadcast Videos"), this project aims to develop a system that extracts human motion from videos and applies it to teach a humanoid robot how to perform similar actions. The primary focus will be on extracting dynamic and expressive motions from videos, such as soccer player celebrations, and using these extracted motions as reference data for reinforcement learning (RL) and imitation learning on a humanoid robot.
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Master Thesis
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Published since: 2025-02-25
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Kaufmann Manuel , Li Chenhao , Li Chenhao , Kaufmann Manuel , Li Chenhao
Topics Engineering and Technology
Learning Agile Dodgeball Behaviors for Humanoid Robots
Agility and rapid decision-making are vital for humanoid robots to safely and effectively operate in dynamic, unstructured environments. In human contexts—whether in crowded spaces, industrial settings, or collaborative environments—robots must be capable of reacting to fast, unpredictable changes in their surroundings. This includes not only planned navigation around static obstacles but also rapid responses to dynamic threats such as falling objects, sudden human movements, or unexpected collisions. Developing such reactive capabilities in legged robots remains a significant challenge due to the complexity of real-time perception, decision-making under uncertainty, and balance control. Humanoid robots, with their human-like morphology, are uniquely positioned to navigate and interact with human-centered environments. However, achieving fast, dynamic responses—especially while maintaining postural stability—requires advanced control strategies that integrate perception, motion planning, and balance control within tight time constraints. The task of dodging fast-moving objects, such as balls, provides an ideal testbed for studying these capabilities. It encapsulates several core challenges: rapid object detection and trajectory prediction, real-time motion planning, dynamic stability maintenance, and reactive behavior under uncertainty. Moreover, it presents a simplified yet rich framework to investigate more general collision avoidance strategies that could later be extended to complex real-world interactions. In robotics, reactive motion planning for dynamic environments has been widely studied, but primarily in the context of wheeled robots or static obstacle fields. Classical approaches focus on precomputed motion plans or simple reactive strategies, often unsuitable for highly dynamic scenarios where split-second decisions are critical. In the domain of legged robotics, maintaining balance while executing rapid, evasive maneuvers remains a challenging problem. Previous work on dynamic locomotion has addressed agile behaviors like running, jumping, or turning (e.g., Hutter et al., 2016; Kim et al., 2019), but these movements are often planned in advance rather than triggered reactively. More recent efforts have leveraged reinforcement learning (RL) to enable robots to adapt to dynamic environments, demonstrating success in tasks such as obstacle avoidance, perturbation recovery, and agile locomotion (Peng et al., 2017; Hwangbo et al., 2019). However, many of these approaches still struggle with real-time constraints and robustness in high-speed, unpredictable scenarios. Perception-driven control in humanoids, particularly for tasks requiring fast reactions, has seen advances through sensor fusion, visual servoing, and predictive modeling. For example, integrating vision-based object tracking with dynamic motion planning has enabled robots to perform tasks like ball catching or blocking (Ishiguro et al., 2002; Behnke, 2004). Yet, dodging requires a fundamentally different approach: instead of converging toward an object (as in catching), the robot must predict and strategically avoid the object’s trajectory while maintaining balance—often in the presence of limited maneuvering time. Dodgeball-inspired robotics research has been explored in limited contexts, primarily using wheeled robots or simplified agents in simulations. Few studies have addressed the challenges of high-speed evasion combined with the complexities of humanoid balance and multi-joint coordination. This project aims to bridge that gap by developing learning-based methods that enable humanoid robots to reactively avoid fast-approaching objects in real time, while preserving stability and agility.
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Master Thesis
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Published since: 2025-02-25
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Engineering and Technology
Learning Real-time Human Motion Tracking on a Humanoid Robot
Humanoid robots, designed to mimic the structure and behavior of humans, have seen significant advancements in kinematics, dynamics, and control systems. Teleoperation of humanoid robots involves complex control strategies to manage bipedal locomotion, balance, and interaction with environments. Research in this area has focused on developing robots that can perform tasks in environments designed for humans, from simple object manipulation to navigating complex terrains. Reinforcement learning has emerged as a powerful method for enabling robots to learn from interactions with their environment, improving their performance over time without explicit programming for every possible scenario. In the context of humanoid robotics and teleoperation, RL can be used to optimize control policies, adapt to new tasks, and improve the efficiency and safety of human-robot interactions. Key challenges include the high dimensionality of the action space, the need for safe exploration, and the transfer of learned skills across different tasks and environments. Integrating human motion tracking with reinforcement learning on humanoid robots represents a cutting-edge area of research. This approach involves using human motion data as input to train RL models, enabling the robot to learn more natural and human-like movements. The goal is to develop systems that can not only replicate human actions in real-time but also adapt and improve their responses over time through learning. Challenges in this area include ensuring real-time performance, dealing with the variability of human motion, and maintaining stability and safety of the humanoid robot.
Keywords
real-time, humanoid, reinforcement learning, representation learning
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Master Thesis
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Published since: 2025-02-25
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Information, Computing and Communication Sciences
Loosely Guided Reinforcement Learning for Humanoid Parkour
Humanoid robots hold the promise of navigating complex, human-centric environments with agility and adaptability. However, training these robots to perform dynamic behaviors such as parkour—jumping, climbing, and traversing obstacles—remains a significant challenge due to the high-dimensional state and action spaces involved. Traditional Reinforcement Learning (RL) struggles in such settings, primarily due to sparse rewards and the extensive exploration needed for complex tasks. This project proposes a novel approach to address these challenges by incorporating loosely guided references into the RL process. Instead of relying solely on task-specific rewards or complex reward shaping, we introduce a simplified reference trajectory that serves as a guide during training. This trajectory, often limited to the robot's base movement, reduces the exploration burden without constraining the policy to strict tracking, allowing the emergence of diverse and adaptable behaviors. Reinforcement Learning has demonstrated remarkable success in training agents for tasks ranging from game playing to robotic manipulation. However, its application to high-dimensional, dynamic tasks like humanoid parkour is hindered by two primary challenges: Exploration Complexity: The vast state-action space of humanoids leads to slow convergence, often requiring millions of training steps. Reward Design: Sparse rewards make it difficult for the agent to discover meaningful behaviors, while dense rewards demand intricate and often brittle design efforts. By introducing a loosely guided reference—a simple trajectory representing the desired flow of the task—we aim to reduce the exploration space while maintaining the flexibility of RL. This approach bridges the gap between pure RL and demonstration-based methods, enabling the learning of complex maneuvers like climbing, jumping, and dynamic obstacle traversal without heavy reliance on reward engineering or exact demonstrations.
Keywords
humanoid, reinforcement learning, loosely guided
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Master Thesis
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Published since: 2025-02-25
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Information, Computing and Communication Sciences
Learning World Models for Legged Locomotion
Model-based reinforcement learning learns a world model from which an optimal control policy can be extracted. Understanding and predicting the forward dynamics of legged systems is crucial for effective control and planning. Forward dynamics involve predicting the next state of the robot given its current state and the applied actions. While traditional physics-based models can provide a baseline understanding, they often struggle with the complexities and non-linearities inherent in real-world scenarios, particularly due to the varying contact patterns of the robot's feet with the ground. The project aims to develop and evaluate neural network-based models for predicting the dynamics of legged environments, focusing on accounting for varying contact patterns and non-linearities. This involves collecting and preprocessing data from various simulation environment experiments, designing neural network architectures that incorporate necessary structures, and exploring hybrid models that combine physics-based predictions with neural network corrections. The models will be trained and evaluated on prediction autoregressive accuracy, with an emphasis on robustness and generalization capabilities across different noise perturbations. By the end of the project, the goal is to achieve an accurate, robust, and generalizable predictive model for the forward dynamics of legged systems.
Keywords
forward dynamics, non-smooth dynamics, neural networks, model-based reinforcement learning
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Master Thesis
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Published since: 2025-02-25
Organization Robotic Systems Lab
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Engineering and Technology
Exploring upper limb impairments using explainable AI on Virtual Peg Insertion Test data
This thesis aims to apply explainable AI techniques to analyze time series data from the Virtual Peg Insertion Test (VPIT), uncovering additional metrics that describe upper limb impairments in neurological subjects, such as those with stroke, Parkinson's disease, and multiple sclerosis. By preserving the full dimensionality of the data, the project will identify new patterns and insights to aid in understanding motor dysfunctions and support rehabilitation.
Keywords
Machine learning, rehabilitation, neurology, upper limb, impairment, explainable AI, SHAP, novel technology, assessment, computer vision, artificial intelligence
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Master Thesis
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Published since: 2025-02-18 , Earliest start: 2025-03-09
Organization Rehabilitation Engineering Lab
Hosts Domnik Nadine
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Comparing the Virtual Peg Insertion Test (VPIT) with the haptic device Inverse3 for assessing upper limb function
This thesis will compare the Virtual Peg Insertion Test (VPIT) with the Inverse3 haptic device by Haply to evaluate its effectiveness as a tool for assessing upper limb function. The focus will be on comparing both the hardware features and software capabilities to determine if the Inverse3 can serve as a valid alternative to VPIT for clinical assessments.
Keywords
Haptic device, virtual environment, rehabilitation, programming, health technology, assessment, software, hardware
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Collaboration , Master Thesis
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Published since: 2025-02-18 , Earliest start: 2025-03-16
Organization Rehabilitation Engineering Lab
Hosts Domnik Nadine
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Benefits and challenges of promoting minimally supervised therapy in a rehabilitation clinic
Increasing the therapy time can benefit patients in multiple ways. Group therapy (e.g., technology-assisted) allows clinics to increase the therapy dose for patients without increasing the workload for therapists. However, in practical implementation, some challenges often arise (e.g., patients not liking it) that limit the efficacy of group therapy. The aim of this project is to gain practical experience with group therapy sessions (i.e., supporting group therapy sessions at the Clinica Hildebrand in Brissago), identify barriers and benefits related to group therapy, and propose guidelines to improve the integration of group therapy in clinical practice.
Keywords
practical internship, research internship, clinical work, technology-assisted therapy, group therapy, minimally supervised therapy
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Internship
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Published since: 2025-02-12 , Earliest start: 2025-03-01 , Latest end: 2025-10-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Università della Svizzera italiana
Organization Rehabilitation Engineering Lab
Hosts Devittori Giada
Topics Medical and Health Sciences
Supervised learning for loco-manipulation
To spot arm operations, we propose a multi-phase approach combining supervised learning and reinforcement learning (RL). First, we will employ supervised learning to develop a model for solving inverse kinematics (IK), enabling precise joint angle calculations from desired end-effector pose. Next, we will utilize another supervised learning technique to build a collision avoidance model, trained to predict and avoid self-collisions based on arm configurations and environmental data. With these pre-trained networks, we will then integrate RL to generate dynamic and safe arm-motion plans. The RL agent will leverage the IK and collision avoidance models to optimize arm trajectories, ensuring efficient and collision-free movements. This entire pipeline could be back propagated while promising to enhance the accuracy, safety, and flexibility of robotic arm operations in complex environments.
Keywords
Spot, Supervised learning, loco-manipulation
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-10 , Earliest start: 2025-02-10 , Latest end: 2026-03-01
Organization Robotic Systems Lab
Hosts Mirrazavi Sina
Topics Information, Computing and Communication Sciences
Model-Based Reinforcement Learning for Loco-manipulation
This project aims to develop a model-based reinforcement learning (RL) framework to enable quadruped robots to perform dynamic locomotion and manipulation simultaneously by leveraging advanced model-based RL algorithms such as DeamerV3, TDMPC2 and SAM-RL. We will develop control policies that can predict future states and rewards, enabling the robot to adapt its behavior on-the-fly. The primary focus will be on achieving stable and adaptive walking patterns while reaching and grasping objects. The outcome will provide insights into the integration of complex behaviors in robotic systems, with potential applications in service robotics and automated object handling.
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-10 , Earliest start: 2025-02-10 , Latest end: 2026-02-10
Organization Robotic Systems Lab
Hosts Mirrazavi Sina
Topics Information, Computing and Communication Sciences
Integrating OpenVLA for Vision-Language-Driven Loco-Manipulation robotics scenarios
This thesis proposes to integrate and adapt the OpenVLA (Open-Source Vision-Language-Action) model to control the Spot robotic arm for performing complex grasping and placing tasks. The study will focus on enabling the robot to recognize, grasp, and organize various toy-sized kitchen items based on human instructions. By leveraging OpenVLA's robust multimodal capabilities, this project aims to bridge the gap between human intent and robotic actions, enabling seamless task execution in unstructured environments. The research will explore the feasibility of fine-tuning OpenVLA for task-specific operations and evaluate its performance in real-world scenarios, providing valuable insights for advancing multimodal robotics.
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-10 , Earliest start: 2025-02-10 , Latest end: 2026-02-10
Organization Robotic Systems Lab
Hosts Mirrazavi Sina
Topics Information, Computing and Communication Sciences
Differentiable Simulation for Precise End-Effector Tracking
Unlock the potential of differentiable simulation on ALMA, a quadrupedal robot equipped with a robotic arm. Differentiable simulation enables precise gradient-based optimization, promising greater tracking accuracy and efficiency compared to standard reinforcement learning approaches. This project dives into advanced simulation and control techniques, paving the way for improvements in robotic trajectory tracking.
Keywords
Differentiable Simulation, Learning, ALMA
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-02-07 , Earliest start: 2025-01-27
Organization Robotic Systems Lab
Hosts Mittal Mayank , Schwarke Clemens , Klemm Victor
Topics Information, Computing and Communication Sciences
Research Assistant in Biosensing for Robotics Care and Body Simulation (~12 months)
Join a team of scientists improving the long-term prognosis and treatment of Spinal Cord Injury (SCI) through mobile and wearable systems and personalized health monitoring. Joining the SCAI Lab part of the Sensory-Motor Systems Lab at ETH, you will have the unique opportunity of working at one of the largest and most prestigious health providers in Switzerland: Swiss Paraplegic Center (SPZ) in Nottwil (LU).
Keywords
App development, Machine Learning, Data bases, Data engineering, Systems Engineering, Data Modelling
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Internship , Lab Practice , Student Assistant / HiWi , ETH Zurich (ETHZ)
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Published since: 2025-02-06 , Earliest start: 2025-03-03 , Latest end: 2026-12-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , IBM Research Zurich Lab , Institute for Research in Biomedicine , Hochschulmedizin Zürich , Swiss Institute of Bioinformatics , University of Lucerne , University of Zurich , Zurich University of Applied Sciences , Zurich University of the Arts , Lucerne University of Applied Sciences and Arts , Berner Fachhochschule
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.
Topics Information, Computing and Communication Sciences
Master's Thesis: AI-powered nap detection from Fitbit data
The uprise of consumer-grade fitness trackers has opened the doors to long-term activity monitoring in the wild in research and clinics. However, Fitbit does not identify napping episodes shorter than 90 minutes. Hence, there is a need to establish a robust algorithm to detect naps.
Keywords
Data analysis, machine learning, signal processing, wearables, Fitbit, naps detection
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Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-06 , Earliest start: 2025-02-10 , Latest end: 2025-08-31
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Gnarra Oriella , Gnarra Oriella
Topics Information, Computing and Communication Sciences , Engineering and Technology
Modeling and Simulation for Earthwork in Digital Twin
In this work, we aim to build a digital twin of our autonomous hydraulic excavator, leveraging Mathworks technology for high-fidelity modeling. This will be used in the future to test and benchmark our learning-based controllers.
Keywords
Modeling, Hydraulics, Excavation, Industry
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-06 , Earliest start: 2025-03-03
Organization Robotic Systems Lab
Hosts Spinelli Filippo , Nan Fang
Topics Information, Computing and Communication Sciences , Engineering and Technology
Reinforcement Learning for Excavation Planning In Terra
We aim to develop a reinforcement learning-based global excavation planner that can plan for the long term and execute a wide range of excavation geometries. The system will be deployed on our legged excavator.
Keywords
Keywords: Reinforcement learning, task planning
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Semester Project , Master Thesis
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Published since: 2025-02-03 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
Model Based Reinforcement Learning
We want to train an excavator agent to learn in a variety of soil using a fast, GPU-accelerated soil particle simulator in Isaac Sim.
Keywords
particle simulation, omniverse, warp, reinforcement learning, model based reinforcement learning.
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Semester Project , Master Thesis
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Published since: 2025-02-03 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Egli Pascal Arturo , Terenzi Lorenzo
Topics Information, Computing and Communication Sciences , Engineering and Technology
Reinforcement Learning for Particle-Based Excavation in Isaac Sim
We want to train RL agents on our new particle simulator, accelerated on the GPU via warp in Isaac sim.
Keywords
particle simulation, omniverse, warp, reinforcement learning
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Semester Project , Master Thesis
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Published since: 2025-02-03 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Egli Pascal Arturo , Mittal Mayank , Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
Perceptive Reinforcement Learning for Exavation
In this project, our goal is to leverage precomputed embeddings(VAE in Isaacsim) from 3D earthworks scene reconstructions to train reinforcement learning agents. These embeddings, derived from incomplete point cloud data and reconstructed using an encoder-decoder neural network, will serve as latent representations. The main emphasis is on utilizing these representations to develop and train reinforcement learning policies for digging tasks.
Keywords
LIDAR, 3D reconstruction, Isaac gym, deep learning, perception, reinforcement learning
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Semester Project , Master Thesis
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Published since: 2025-02-03 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Höller David , Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
Depth Estimation with Event-Based Cameras on nano-UAVs
develop a neural network for depth estimation on nano-drones, using miniaturized event-based cameras, for autonomous navigation
Keywords
nano-drones, robotics, autonomous navigation, depth-estimation, event-based cameras, neuromorphic, neuromorphic vision, embedded systems, tinyML, artificial intelligence, deep learning.
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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-03 , Earliest start: 2025-02-03 , Latest end: 2025-12-31
Organization Digital Circuits and Systems (Benini)
Hosts Lamberti Lorenzo
Topics Engineering and Technology
Digital HW design: accelerator for Event-Based Convolutional Neural Network
Digital HW design: accelerator for Event-Based Convolutional Neural Network
Keywords
Digital HW design, PULP, RISC-V
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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-03 , Earliest start: 2025-02-03 , Latest end: 2025-07-31
Organization Digital Circuits and Systems (Benini)
Hosts Lamberti Lorenzo
Topics Engineering and Technology
Artificial intelligence and perception on insect sized robots
Maze escape with tiny AI-based autonomous bugs
Keywords
Insect-sized robots, robotics, artificial intelligence, embedded systems, autonomous navigation
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Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-03 , Latest end: 2025-07-01
Organization Digital Circuits and Systems (Benini)
Hosts Lamberti Lorenzo
Topics Engineering and Technology
Autonomous nano-drone racing CNN on next-gen ULP SoC
Develop a visual-based neural network for autonomous nano-drone racing
Keywords
nano-drones, robotics, autonomous navigation, embedded systems,artificial intellicence, deep learning, microcontroller
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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-03 , Latest end: 2025-07-31
Organization Digital Circuits and Systems (Benini)
Hosts Lamberti Lorenzo
Topics Engineering and Technology
Reiforcement Learning of Pretrained Trasformer Models
We want to train RL agents on our new particle simulator, accelerated on the GPU via warp in Isaac sim.
Keywords
Keywords: particle simulation, omniverse, warp, reinforcement learning
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Semester Project , Master Thesis
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Published since: 2025-02-03 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
Multiagent Reinforcement Learning in Terra
We want to train multiple agents in the Terra environment, a fully end-to-end GPU-accelerated environment for RL training.
Keywords
multiagent reinforcement learning, jax, deep learning, planning
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Semester Project , Master Thesis
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Published since: 2025-02-03 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
Hand-object 3D reconstruction from Internet videos (Computer Vision)
The goal of the project is to reconstruct 3D hands and objects from internet videos.
Keywords
Computer vision, VR/AR, 3D reconstruction, 3D pose estimation, machine learning, neural networks, human-object interactions
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Semester Project , Master Thesis
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Published since: 2025-02-01 , Earliest start: 2024-11-18 , Latest end: 2025-08-20
Applications limited to ETH Zurich , Department of Computer Science , Eberhard Karls Universität Tübingen
Organization Advanced Interactive Technologies
Hosts Fan Zicong
Topics Information, Computing and Communication Sciences
A Bayesian sensor fusion and machine learning approach for robust hand gesture decoding with application to stroke rehabilitation.
About the project: This thesis aims to design a framework for robust fine-motor action decoding using multi-modal (sEMG and depth sensing camera) Bayesian sensor fusion and machine learning approach
Keywords
Bayesian inference, sEMG, depth sensing camera, rehabilitation, machine learning, deep transfer learning
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-02-01 , Earliest start: 2025-03-01 , Latest end: 2025-08-01
Organization Sensory-Motor Systems Lab
Hosts Dash Adyasha
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Data Driven Simulation for End-to-End Navigation
Investigate how neural rendering can become the backbone of comprehensive, next generation data-driven simulation
Keywords
Neural rendering, Simulation
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Internship , Master Thesis
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Published since: 2025-01-24 , Earliest start: 2025-01-27
Organization Robotic Systems Lab
Hosts Kneip Laurent
Topics Information, Computing and Communication Sciences , Engineering and Technology
Master Thesis: Data Analysis of Wearable and Nearable Sensors Data for Classification of Activities of Daily Living
This project aims to develop a novel algorithm for tracking a person's health condition changes using daily life wearable sensor data, biosignals, and information from nearable sensors. With the Life-long-logging system, we want to provide meaningful data for medical staff and directly engage patients and their caregivers.
Keywords
Data analysis, Machine Learning, Wearable Sensors
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Semester Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-01-22 , Earliest start: 2025-02-03 , Latest end: 2025-09-30
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Gnarra Oriella , Gnarra Oriella
Topics Information, Computing and Communication Sciences , Engineering and Technology
Master Thesis: Data Analysis of Wearable and Nearable Sensors Data within the StrongAge Cohort Study
The StrongAge Dataset, collected over one year, provides a rich data repository from unobtrusive, contactless technologies combined with validated mood and cognition questionnaires. This project aims to uncover digital biomarkers that can transform elderly care, addressing critical research questions related to sleep, cognition, physical activity, and environmental influences.
Keywords
Data analysis, Machine learning, Wearable and Nearable Sensors Data
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Semester Project , Internship , Lab Practice , Bachelor Thesis , Master Thesis , Student Assistant / HiWi , ETH Zurich (ETHZ)
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Published since: 2025-01-22 , Earliest start: 2025-02-03 , Latest end: 2025-09-30
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Gnarra Oriella , Gnarra Oriella
Topics Information, Computing and Communication Sciences , Engineering and Technology
Reconstructing liquids from multiple views with 3D Gaussian Splatting
This project reconstructs liquids from multi-view imagery, segmenting fluid regions using methods like Mask2Former and reconstructing static scenes with 3D Gaussian Splatting or Mast3r. The identified fluid clusters initialize a particle-based simulation, refined for temporal consistency and enhanced by optional thermal data and visual language models for fluid properties.
Keywords
3D reconstruction, Gaussian Splatting, physics simulation
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Semester Project , Master Thesis
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Published since: 2025-01-08
Organization Computer Vision and Geometry Group
Hosts Barath Daniel , Tsalicoglou Christina
Topics Information, Computing and Communication Sciences
A generalized sEMG-based gesture recognition framework using deep learning approach
This thesis aims to develop a generalizable (user-invariant and session-invariant) gesture recognition framework using deep neural networks
Keywords
sEMG, deep learning, manifold, gesture decoding
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Master Thesis
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Published since: 2025-01-05 , Earliest start: 2025-02-01 , Latest end: 2026-03-01
Organization Sensory-Motor Systems Lab
Hosts Dash Adyasha
Topics Information, Computing and Communication Sciences , Engineering and Technology
Unraveling Calcium Dynamics and Immune Interactions in Bone Graft Substitute Environments through Advanced Ratiometric Imaging
This project endeavors to explore the dynamic interplay among calcium ions, bone graft substitutes, and resident immune cells in both orthotopic and ectopic environments, employing advanced ratiometric imaging techniques.
Keywords
Bone Graft Substitute, Calcium, Ratiometric Imaging, Immune Cells, in vitro, in vivo, Intravital Microscopy
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-12-24 , Earliest start: 2024-10-01 , Latest end: 2025-06-30
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Wissmann Stefanie
Topics Engineering and Technology , Biology
Developing a real-time state-machine for closed-loop brain-machine interfaces
Developing a state-machine Simulink model to be deployed at MathWorks SpeedGoat real-time target machine for closed-loop brain-machine interface (BMI). The state-machine will control the closed-loop BMI peripherals and synchronise the data flow. Peripherals include neural recorders & stimulators, data analysis cluster, video cameras and experimental chamber. Experimental chamber (variety of servos, steppers, sensors etc.) will be controlled with built-in FPGA and GPIO of SpeedGoat machine. Other peripherals are connected with serial bus. Acquired data needs to be organized and stored in datasink unit. Skills: Matlab Simulink, state-machines, FPGA programming, serial communication protocols, data synchronisation Please send your CV and transcript along with your application.
Keywords
Brain-machine interfaces, neural recording, neural stimulation, state-machines, real-time systems, serial communication, FPGA
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-12-20
Applications limited to EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , University of Zurich , Wyss Translational Center Zurich , Paul Scherrer Institute , CERN , Empa
Organization Neurotechnology
Hosts Özil Eminhan
Topics Engineering and Technology
Developing a GUI for cross-modality brain-machine interfaces
Programming a graphical user interface (e.g. in Qt/C++) which can handle and process the data acquired in our brain-machine interface (BMI) experiments. The data includes high-density brain activity recordings from hundreds of recording channels, neural-stimulation events, 3D&4D data coming from MRI scans of the subject implanted with BMI. The backend will be programmed in Python where you also need to connect supporting tools (e.g. Blender) via Python. Please send an email with your CV and transcript of records attached.
Keywords
Brain-machine interface, BMI, graphical user interface, GUI, Qt, C, C++, Python, Blender, MRI, electrophysiology
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Semester Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-12-19
Applications limited to Empa , EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , IBM Research Zurich Lab , Paul Scherrer Institute , University of Zurich , Wyss Translational Center Zurich
Organization Neurotechnology
Hosts Özil Eminhan
Topics Information, Computing and Communication Sciences , Engineering and Technology
Diffusion-based Shared Autonomy System for Telemanipulation
Robots may not be able to complete tasks fully autonomously in unstructured or unseen environments, however direct teleoperation from human operators may also be challenging due to the difficulty of providing full situational awareness to the operator as well as degradation in communication leading to the loss of control authority. This motivates the use of shared autonomy for assisting the operator thereby enhancing the performance during the task. In this project, we aim to develop a shared autonomy framework for teleoperation of manipulator arms, to assist non-expert users or in the presence of degraded communication. Imitation learning, such as diffusion models, have emerged as a popular and scalable approach for learning manipulation tasks [1, 2]. Additionally, recent works have combined this with partial diffusion to enable shared autonomy [3]. However, the tasks were restricted to simple 2D domains. In this project, we wish to extend previous work in the lab using diffusion-based imitation learning, to enable shared autonomy for non-expert users to complete unseen tasks or in degraded communication environments.
Keywords
Imitation learning, Robotics, Manipulation, Teleoperation
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Semester Project , ETH Zurich (ETHZ)
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Published since: 2024-12-02 , Earliest start: 2024-11-01 , Latest end: 2025-11-01
Applications limited to ETH Zurich , University of Zurich
Organization Robotic Systems Lab
Hosts Elanjimattathil Aravind
Topics Information, Computing and Communication Sciences , Engineering and Technology