Master
Master Theses at IRIS
Developing Multi-Functional Microrobots Using Microfluidic Chips (3M project)
We are looking for a motivated Master’s student to join an exciting interdisciplinary thesis project, collaborating between the Multi-Scale Robotics Lab (D-MAVT) and the deMello group (D-CHAB) at ETH Zurich. This project focuses on creating a novel microfluidic-based bottom-up method to fabricate multifunctional microrobots. This innovative approach seeks to revolutionize microrobot fabrication, opening the door to diverse new applications.
Keywords
Microfluidics, Self-assembly, Microrobots
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-06-22 , Earliest start: 2025-02-17
Organization Multiscale Robotics Lab
Hosts Hu Minghan
Topics Engineering and Technology , Chemistry
Measurement of hip internal rotation range of motion in individuals with hip joint disorders
Together with the Schulthess Clinic, we have developed the mHIRex which, based on the common clinical manoeuvre, precisely determines the force required for internal hip rotation. The next step is to assess hip internal rotation in a large cohort of patients with hip disorders.
Keywords
hip osteoarthritis, femoroacetabular impingement syndrome, clinical examination, biomechanics
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Internship , Master Thesis
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Published since: 2025-06-20 , Earliest start: 2025-09-24 , Latest end: 2026-12-31
Applications limited to Department of Health Sciences and Technology , Department of Mechanical and Process Engineering
Organization Sensory-Motor Systems Lab
Hosts Wolf Peter
Topics Medical and Health Sciences , 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 , Master Thesis
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Published since: 2025-06-18 , Earliest start: 2025-03-24 , Latest end: 2026-08-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Kateb Pierre
Topics Engineering and Technology
RL Finetuning for Generalized Quadruped Locomotion
This project investigates the potential of reinforcement learning (RL) fine-tuning to develop a single, universal locomotion policy for quadruped robots. Building on prior work in multi-terrain skill synthesis [1], we will probe the limits of generalization by systematically fine-tuning on an ever-expanding set of diverse environments. This incremental approach will test the hypothesis that a controller can learn to robustly navigate a vast range of terrains. As a potential extension, procedural terrain generation may be used to automatically create novel challenges, pushing the boundaries of policy robustness.
Keywords
Reinforcement Learning, Quadruped Locomotion
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Master Thesis
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Published since: 2025-06-17 , Earliest start: 2025-06-15
Organization Robotic Systems Lab
Hosts Schwarke Clemens , He Junzhe
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: 2025-06-17 , Earliest start: 2025-01-27
Organization Robotic Systems Lab
Hosts Mittal Mayank , Schwarke Clemens , Klemm Victor
Topics Information, Computing and Communication Sciences
AI-Driven Push Notifications for Monitoring and Enhancing Adherence in At-Home Neurorehabilitation
Adherence to rehabilitation therapy is crucial for the recovery of hand functionality in stroke and traumatic brain injury (TBI) patients. However, sustaining patient motivation to train at home remains a challenge. This project aims to explore the impact of push notifications delivered via LLM chatbots on adherence to physical therapy among stroke and TBI patients. By investigating the optimal frequency and content of notifications, the goal is to develop an AI-driven notification/reminder system that fosters continuous engagement with the rehabilitation plan, ultimately promoting increased therapy and better functional outcomes for patients.
Keywords
App Development, Stroke, Traumatic Brain Injury, Rehabilitation, Adherence to Therapy, Push Notifications, mHealth Apps, Large Language Models, Interdisciplinary Research, React Native
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-06-17 , Earliest start: 2025-06-22 , Latest end: 2026-09-01
Organization Rehabilitation Engineering Lab
Hosts Retevoi Alexandra
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Learning a Simulation-Trained Safety Critic for Safe Online Learning in Legged Robots
This project focuses on developing a safety critic—a model that predicts the safety of robot states—to enable safe online learning on legged robotic hardware. The safety critic is trained in simulation using labeled data from diverse robot behaviors, identifying states likely to lead to failure (e.g., falls). Once trained, the critic is deployed alongside a learning policy to restrict unsafe exploration, either by filtering dangerous actions or shaping the reward function. The goal is to allow adaptive behavior on real hardware while minimizing physical risk.
Keywords
safety critic, online learning
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Master Thesis
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Published since: 2025-06-16
Organization Robotic Systems Lab
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics 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: 2025-06-16 , Earliest start: 2025-01-27
Organization Robotic Systems Lab
Hosts Kneip Laurent
Topics Information, Computing and Communication Sciences , Engineering and Technology
AI-Based Motion Estimation and Fatigue Monitoring Using Triboelectric Nanogenerators
This project explores the feasibility of using triboelectric nanogenerators (TENGs) for joint angle analysis and fatigue monitoring during repetitive human movements using machine learning and deep learning.
Keywords
machine learning, artificial intelligence, generative AI, triboelectric nanogenerator, joint angle estimation, motion analysis, fatigue detection
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Semester Project , Master Thesis
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Published since: 2025-06-16 , Earliest start: 2025-07-01 , Latest end: 2026-06-30
Organization Biomedical and Mobile Health Technology Lab
Hosts Otesteanu Corin, Dr
Topics Information, Computing and Communication 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: 2025-06-16 , 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: 2025-06-16 , Earliest start: 2025-03-17
Organization Robotic Systems Lab
Hosts Kneip Laurent
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: 2025-06-15 , Earliest start: 2025-06-20
Organization Biomedical and Mobile Health Technology Lab
Hosts Li Yuanlong
Topics Engineering and Technology
AI-Based Estimation of Blood Pressure via Pulse Transit Time and Vascular Dynamics from piezoelectric sensors
This project explores the development and validation of an AI based system for non-invasive blood pressure estimation. The method focuses on deriving pulse transit time (PTT) using dual A-mode piezoelectric sensors (ultrasound) and characterizing vascular features such as arterial wall diameter and flow velocity. The work contributes toward a future wearable ultrasound-based solution for continuous cardiovascular monitoring.
Keywords
machine learning, deep learning, artificial intelligence, blood pressure, pulse transit time, vascular imaging, Doppler
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Semester Project , Master Thesis
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Published since: 2025-06-12 , Earliest start: 2025-06-30 , Latest end: 2026-06-30
Organization Biomedical and Mobile Health Technology Lab
Hosts Otesteanu Corin, Dr
Topics Information, Computing and Communication Sciences , Engineering and Technology
Activity and fatigue detection using machine learning based on real-world data from smart clothing
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.
Keywords
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: 2025-06-12 , Earliest start: 2025-07-01 , Latest end: 2026-03-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Ahmadizadeh Chakaveh
Topics Information, Computing and Communication Sciences , Engineering and Technology
Design data acquisition solution for smart clothing
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.
Keywords
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: 2025-06-12 , Earliest start: 2025-07-01 , Latest end: 2025-12-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Ahmadizadeh Chakaveh
Topics Information, Computing and Communication Sciences , Engineering and Technology
Vision-Based Agile Aerial Transportation
Develop a vision-based aerial transportation system with reinforcement / imitation learning.
Keywords
aerial transportation, reinforcement learning (RL), drones, robotics
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Master Thesis
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Published since: 2025-06-12 , Earliest start: 2025-05-01 , Latest end: 2026-02-28
Organization Robotics and Perception
Hosts Geles Ismail
Topics Information, Computing and Communication Sciences , Engineering and Technology
Reinforcement Learning for Drone Maneuvers from Human Preferences
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: 2025-06-12 , Earliest start: 2024-05-15 , Latest end: 2026-04-01
Organization Robotics and Perception
Hosts Geles Ismail
Topics Information, Computing and Communication Sciences , Engineering and Technology
Personalized Low Latency Interactive AI Project
We are seeking one highly motivated student to join our innovative project focused on developing a cutting-edge voice recognition and personalization platform for wheelchair users This project aims to deliver low-latency, context-aware, and personalized AI interactions in noisy, multi-user environments, leveraging advanced models and distilled LLMs, combined with biosignal tracking and GDPR-compliant data management.
Keywords
Voice recognition, AI personalization, low latency, LLMs, biosignal tracking, neurofeedback, multi-user environments, audio processing
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Semester Project , Internship , Master Thesis
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Published since: 2025-06-11 , Earliest start: 2025-08-01
Organization Sensory-Motor Systems Lab
Hosts Paez Diego, Dr.
Topics Engineering and Technology
Learning to Socially Navigate in Crowds using RL
This project aims to develop a robotic planner that can safely navigate crowded environments, considering human movement patterns and social norms. It seeks to overcome limitations of current planners, which either require privileged information or can't handle semantic constraints. The goal is to create a robust planner for real robots (ANYmal or Unitree B2W) that works in dynamic, constrained environments. Challenges include training an RL policy, expanding movement patterns, and transferring from simulation to real hardware.
Keywords
Reinforcement Learning, Navigation, Planning, Robotics, Legged Robotics, Simulation
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Master Thesis
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Published since: 2025-06-11
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Robotic Systems Lab
Hosts Roth Pascal
Topics Information, Computing and Communication Sciences
Active System Identification for Efficient Online Adaptation
This project proposes a novel single-stage training framework for system identification in legged locomotion, addressing limitations in the conventional two-stage teacher-student paradigm. Traditionally, a privileged teacher policy is first trained with full information, followed by a student policy that learns to mimic the teacher using only state-action histories—resulting in suboptimal exploration and limited adaptability. In contrast, our method directly trains a policy to regress privileged information embeddings from its history while simultaneously optimizing for an active exploration objective. This objective is based on maximizing mutual information between the policy’s state-action trajectories and the privileged latent variables, encouraging exploration of diverse dynamics and enhancing online adaptability. The approach is expected to improve sample efficiency and robustness in deployment environments with variable dynamics.
Keywords
Active Exploration, System Identification, Online Adaptation
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Master Thesis
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Published since: 2025-06-06
Organization Robotic Systems Lab
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Engineering and Technology
Learning from Online Demonstrations via Video Diffusion for Local Navigation
This project introduces a framework for local navigation skill acquisition through online learning from demonstrations, bypassing the need for offline expert trajectories. Instead of relying on pre-collected data, we use video diffusion models conditioned on semantic text prompts to generate synthetic demonstration videos in real time. These generated sequences serve as reference behaviors, and the agent learns to imitate them via an image-space reward function. The navigation policy is built atop a low-level locomotion controller and targets deployment on legged platforms such as humanoids and quadrupeds. This approach enables semantically guided, vision-based navigation learning with minimal human supervision and strong generalization to diverse environments.
Keywords
Learning from Demonstrations, Video Diffusion, Semantic Conditioning
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Master Thesis
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Published since: 2025-06-06
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 (Structured legged world model)
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: 2025-06-06
Organization Robotic Systems Lab
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Engineering and Technology
All-textile Wearable Thermochromic Displays
The goal of the project is to develop a technology for information display on textile utilizing thermochromism phenomenon.
Keywords
wearable, display, textile, thermochromism, e-textile, fabric
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Bachelor Thesis , Master Thesis
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Published since: 2025-06-05 , Earliest start: 2025-06-01
Organization Biomedical and Mobile Health Technology Lab
Hosts Shokurov Aleksandr
Topics Medical and Health Sciences , Engineering and Technology
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: 2025-06-05 , Earliest start: 2025-06-01 , Latest end: 2026-04-01
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: 2025-06-05 , 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
How to Touch: Exploring Tactile Representations for Reinforcement Learning
Developing and benchmarking tactile representations for dexterous manipulation tasks using reinforcement learning.
Keywords
Reinforcement Learning, Dexterous Manipulation, Tactile Sensing
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-06-04 , Earliest start: 2024-12-15 , Latest end: 2025-06-01
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Bhardwaj Arjun , Zurbrügg René
Topics Information, Computing and Communication Sciences
AI-Driven Rock Reshaping Simulation and Control
This project develops an intelligent system for controlling rock fracture by combining finite element analysis (FEM) with machine learning. FEM simulations train a graph neural network (GNN) to predict fracture patterns. A reinforcement learning (RL) agent then uses this predictive GNN to learn optimal actions for guiding fractures towards a desired rock geometry, enabling precise and goal-oriented control.
Keywords
machine learning, deep learning, reinforcement learning, graph neural networks, construction robotics, space robotics
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Semester Project , Master Thesis
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Published since: 2025-06-02 , Earliest start: 2025-07-07
Organization Robotic Systems Lab
Hosts Spinelli Filippo
Topics Information, Computing and Communication Sciences , Engineering and Technology
Perceptive Arm Motion Planning and Control for Heavy Construction Machine Tasks
In this work we would utilize reinforcement learning, neural network actuator modeling, and perception for the control and arm motion planning of a 40ton excavator with a free-swinging gripper. The project will be in collaboration with Gravis Robotics, ETH spinoff working on the automation of heavy machinery.
Keywords
reinforcement learning, perception, hydraulics, excavator, manipulation, industry
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Semester Project , Collaboration , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-06-02 , Earliest start: 2025-07-07
Organization Robotic Systems Lab
Hosts Egli Pascal Arturo , Terenzi Lorenzo , Spinelli Filippo
Topics Information, Computing and Communication Sciences , Engineering and Technology
Bridging the Gap: Enabling Soft Actor-Critic for High-Performance Legged Locomotion
Proximal Policy Optimization (PPO) has become the de facto standard for training legged robots, thanks to its robustness and scalability in massively parallel simulation environments like IsaacLab. However, alternative algorithms such as Soft Actor-Critic (SAC), while sample-efficient and theoretically appealing due to entropy maximization, have not matched PPO’s empirical success in this domain. This project aims to close that performance gap by developing and evaluating modifications to SAC that improve its stability, scalability, and sim-to-real transferability on legged locomotion tasks. We benchmark SAC against PPO using standardized pipelines and deploy learned policies on real-world quadruped hardware, pushing toward more flexible and efficient reinforcement learning solutions for legged robotics.
Keywords
Legged locomotion, Soft Actor-Critic, Reinforcement learning, Sim-to-real transfer
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Master Thesis
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Published since: 2025-05-30
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Information, Computing and Communication Sciences
Development of Neuromuscular Biohybrid Robots
Biohybrid robots integrate living cells and synthetic components to achieve motion. These systems often rely on engineered skeletal muscle tissues that contract upon electrical stimulation for actuation. Neuromuscular-powered biohybrid robots take this concept further by integrating motor neurons to induce muscle contractions, mimicking natural muscle actuation. In our lab, we are developing neuromuscular actuators using advanced 3D co-culture systems and biofabrication techniques to enable functional macro-scale biohybrid robots.
Keywords
Tissue engineering, mechanical engineering, biology, neuroengineering, biomaterials, biohybrid robotics, 3D in vitro models, biofabrication, bioprinting, volumetric printing.
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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-05-28 , Earliest start: 2025-06-02
Organization Soft Robotics Lab
Hosts Badolato Asia , Katzschmann Robert, Prof. Dr.
Topics Medical and Health Sciences , Engineering and Technology , Biology
Master Thesis - Signal Processing for Neurological Data
We are offering a Masters thesis project for a motivated student to develop a complete signal processing pipeline tailored to neurological data, with the goal of detecting early biomarkers of cognitive or neurological conditions. This project blends neuroscience, signal processing, and artificial intelligence in a practical and high-impact context.
Keywords
signal processing, neurological data, fNIRS, EEG, neuroimaging, brain-computer interface, biomedical signal processing, artifact removal, noise reduction, ICA, wavelet denoising, feature extraction, FFT, PSD, ERP, hemodynamic response, connectivity analysis, machine learning, AI, classification, clustering, SVM, Random Forest, deep learning, PCA, LDA, anomaly detection, biomarkers, neuroscience, Python, MNE, scikit-learn, PyTorch, TensorFlow, Optohive, ETH Zurich, Relab
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-05-22 , Earliest start: 2025-08-01 , Latest end: 2026-06-01
Organization Rehabilitation Engineering Lab
Hosts Willhaus Marc
Topics Medical and Health Sciences , Engineering and Technology , Biology , Physics
Master Thesis - Deep Learning and AI Modelling of Neurological Data
We are looking for a master student who codevelops AI and machine learning models and inference pipelines on the base of neurological fNIRS sensory data.
Keywords
deep learning, time-series, fNIRS, EEG, EMG, neurotechnology, neurological data, sequence modeling, CNN, LSTM, GRU, Transformer, hybrid models, self-supervised learning, contrastive learning, biomarker discovery, AI, machine learning, brain-computer interface, data augmentation, model interpretability, Grad-CAM, SHAP, saliency maps, biomedical signal processing, PyTorch, TensorFlow, Python, Optohive, ETH Zurich, Relab
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-05-22 , Earliest start: 2025-07-01 , Latest end: 2026-06-01
Organization Rehabilitation Engineering Lab
Hosts Willhaus Marc
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology , Physics
Learning Terrain Traversal from Human Strategies for Agile Robotics
Teaching robots to walk on complex and challenging terrains, such as rocky paths, uneven ground, or cluttered environments, remains a fundamental challenge in robotics and autonomous navigation. Traditional approaches rely on handcrafted rules, terrain classification, or reinforcement learning, but they often struggle with generalization to real-world, unstructured environments.
Keywords
3D reconstruction, egocentric video, SMPL representation
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Semester Project , Master Thesis
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Published since: 2025-05-21 , Earliest start: 2025-05-26
Organization Computer Vision and Geometry Group
Hosts Wang Xi , Frey Jonas , Patel Manthan , Kaufmann Manuel , Li Chenhao
Topics Information, Computing and Communication Sciences
Humanoid Locomotion in Rough Terrain via Imitation Learning
TLDR: Make Humanoid walk in rough terrain using human demonstration and RL
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Semester Project , Master Thesis
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Published since: 2025-05-21 , Earliest start: 2025-05-31 , Latest end: 2025-09-30
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , University of Zurich
Organization Robotic Systems Lab
Hosts Frey Jonas
Topics Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences
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: 2025-05-20 , Earliest start: 2025-06-01
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: 2025-05-20 , Earliest start: 2025-06-01
Organization Rehabilitation Engineering Lab
Hosts Domnik Nadine
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Embedded algorithms of IMUs in a neurorehabilitation device
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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Semester Project , Master Thesis
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Published since: 2025-05-19 , 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 and Testing of Electrical Systems for a SmartVNS Docking Station with Focus on Wireless Data Management
We are looking for an enthusiastic electrical/firmware engineer to design and implement the electrical and firmware aspects of a docking station for the SmartVNS device. The station will charge the device components (pulse generator and wrist motion tracker) and pull data from the pulse generator and motion tracker, uploading it to an online server via Wi-Fi. This project will also involve testing the reliability of data transfer and power systems under real-world conditions, providing valuable insights into the practical application of this technology.
Keywords
Electrical, embedded, electronic, engineering, biomedical
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Internship , Bachelor Thesis , Master Thesis
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Published since: 2025-05-19 , Earliest start: 2024-08-18 , Latest end: 2025-10-01
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius
Topics Information, Computing and Communication Sciences , Engineering and Technology
Development of Regulatory Documentation for a Novel Neurorehabilitation Device: Preparation for FDA and Swissmedic Compliance
Stroke is a leading cause of long-term disability, affecting millions annually and necessitating innovative approaches to rehabilitation. The Rehabilitation Engineering Laboratory (RELab) at ETH Zurich is developing a novel closed-loop neurorehabilitation device that integrates real-time motion tracking with non-invasive brain stimulation to enhance neural plasticity and promote motor recovery in stroke patients. To advance this technology toward clinical trials, comprehensive regulatory documentation is essential to meet the stringent requirements of the U.S. Food and Drug Administration (FDA) and Swissmedic. This project focuses on preparing an Investigational Device Exemption (IDE) application for the FDA and supporting documentation for Swissmedic compliance, including technical descriptions, risk analyses, and clinical study protocols. The student will conduct literature reviews, draft regulatory documents, and support risk management in accordance with ISO 14971, contributing to the device’s regulatory pathway. This work offers a unique opportunity to gain expertise in medical device regulation, bridging biomedical engineering and neuroscience, and advancing a transformative solution for stroke rehabilitation.
Keywords
regulatory affairs, medical device, non-invasive brain stimulation, FDA, Swissmedic, investigational device exemption, IDE, stroke rehabilitation, compliance
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2025-05-19 , Earliest start: 2025-05-25 , Latest end: 2025-08-01
Organization Rehabilitation Engineering Lab
Hosts Donegan Dane , Viskaitis Paulius
Topics Medical and Health Sciences , Engineering and Technology
Global Optimization Enabled by Learning
We aim to characterize optimization landscapes using metrics such as Sobolev norms, measuring function smoothness, Hessian spectral properties, indicating curvature, and the tightness of semidefinite programming (SDP) relaxations (relevant for polynomial optimization). The core innovation lies in translating these metrics into differentiable objectives or regularizers. By incorporating these into the training process, we encourage the learned modules to produce downstream optimization problems that are inherently well-conditioned and possess favourable global structures
Keywords
Optimization, Learning, Optimal, Robotics
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-05-19 , Earliest start: 2025-06-01 , Latest end: 2026-06-01
Organization Robotic Systems Lab
Hosts Talbot William , Tuna Turcan
Topics Engineering and Technology
Strategic Financial Modelling and Business Plan Development for a Breakthrough Neurorehabilitation Device
With over 14 million stroke cases annually, the global neurorehabilitation market presents a multi-billion-dollar opportunity for innovative solutions addressing motor recovery. The Rehabilitation Engineering Laboratory (RELab) at ETH Zurich is developing a revolutionary closed-loop neurorehabilitation device that leverages motion tracking and non-invasive brain stimulation to transform stroke rehabilitation. This project aims to develop a sophisticated financial model and a strategic business plan to propel the device to market leadership. The student will conduct market analysis, build financial projections, and craft a compelling business strategy, focusing on pricing, reimbursement, and investor engagement. By delivering investor-ready materials and a scalable commercialization plan, this work will position the device for rapid market entry and long-term success, offering the student a unique opportunity to blend business strategy, entrepreneurship, and healthcare innovation.
Keywords
financial modelling, business strategy, medical device, neurorehabilitation, startup, stroke rehabilitation, entrepreneurship, market entry, investment
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2025-05-19 , Earliest start: 2025-05-25 , Latest end: 2025-09-01
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius
Topics Medical and Health Sciences , Engineering and Technology , Economics , Commerce, Management, Tourism and Services
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
Fine-tuning Policies in the Real World with Reinforcement Learning
Explore online fine-tuning in the real world of sub-optimal policies.
Keywords
online fine-tuning, reinforcement learning (RL), continual learning, drones, robotics
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Semester Project , Master Thesis
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Published since: 2025-05-13 , Earliest start: 2025-06-01 , Latest end: 2026-04-30
Organization Robotics and Perception
Hosts Geles Ismail
Topics Information, Computing and Communication Sciences , Engineering and Technology
Inverse Reinforcement Learning from Expert Pilots
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: 2025-05-13 , Earliest start: 2025-06-01 , Latest end: 2026-04-30
Organization Robotics and Perception
Hosts Geles Ismail
Topics Information, Computing and Communication Sciences , Engineering and Technology
Advancing Low-Latency Processing for Event-Based Neural Networks
Design and implement efficient event-based networks to achieve low latency inference.
Keywords
Computer Vision, Event Cameras
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Semester Project , Master Thesis
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Published since: 2025-05-12 , Earliest start: 2024-12-12
Organization Robotics and Perception
Hosts Messikommer Nico
Topics Information, Computing and Communication 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
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: 2025-07-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Engineering and Technology
Transcatheter Heart Valve Repair and Replacement Devices at Harvard Medical School
Master thesis on novel devices and tools for both valve repair and replacement at Harvard Medical School
Keywords
Prototyping, Experimental Evaluation, Materials
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Master Thesis
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Published since: 2025-05-02
Applications limited to ETH Zurich
Organization Multiscale Robotics Lab
Hosts Gantenbein Valentin
Topics Medical and Health Sciences , Engineering and Technology
Autonomous Robotic Cardiac Catheters at Harvard Medical School
We are developing robotic catheters for heart valve repair and for treatment of arrythmias.
Keywords
Autonomous Control, Medical devices, Animal models, Prototyping
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Master Thesis
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Published since: 2025-05-02
Applications limited to ETH Zurich
Organization Multiscale Robotics Lab
Hosts Gantenbein Valentin
Topics Medical and Health Sciences , Engineering and Technology
Feedback Optimization of Acoustic Patterning in Real Time for Bioprinter
Our project aims to enhance the ultrasound-assisted bioprinting process using real-time feedback and image processing. We have developed a transparent nozzle equipped with multiple cameras for real-time monitoring. The next steps involve integrating advanced image processing techniques, such as template matching, and implementing a feedback system to optimize the printing process. The system will be fully automated, featuring a function generator for wave creation and cooling elements. By analyzing the printing process and acoustic cell patterning with computer vision and leveraging real-time sensor feedback, we aim to dynamically optimize parameters such as frequency and amplitude for accurate and consistent pattern formation, crucial for bio applications.
Keywords
Machine learning, control and automation, 3D Printing, Ultrasound
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Bachelor Thesis , Master Thesis
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Published since: 2025-04-29 , Earliest start: 2025-02-01 , Latest end: 2025-09-30
Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)
Hosts Medany Mahmoud
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences
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 , Kneip Laurent
Topics Information, Computing and Communication Sciences , Engineering and Technology
Agile Flight of Flexible Drones in Confined Spaces
The project aims to create a controller for an interesting and challenging type of quadrotor, where the rotors are connected via flexible joints.
Keywords
Robotics, Autonomous Systems, Model Predictive Control, Quadcopter, Drone Racing, Approximate Dynamic Programming, Model-Based Reinforcement Learning
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Semester Project , Master Thesis
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Published since: 2025-04-17 , Earliest start: 2025-06-01 , Latest end: 2026-03-01
Organization Robotics and Perception
Hosts Reiter Rudolf
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Vision-Based World Models for Real-Time Robot Control
This project aims to use vision-based world models as a basis for model-based reinforcement learning, aiming to achieve a generalizable approach for drone navigation.
Keywords
Robotics, Autonomous Systems, Computer Vision, Foundation Models, Reinforcement Learning
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Semester Project , Master Thesis
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Published since: 2025-04-17 , Earliest start: 2025-05-01 , Latest end: 2026-02-28
Organization Robotics and Perception
Hosts Reiter Rudolf
Topics Information, Computing and Communication Sciences
Vision-Based Reinforcement Learning in the Real World
We aim to learn vision-based policies in the real world using state-of-the-art model-based reinforcement learning.
Keywords
Robotics, Autonomous Systems, Computer Vision, Reinforcement Learning
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Semester Project , Master Thesis
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Published since: 2025-04-17 , Earliest start: 2025-05-01 , Latest end: 2026-02-01
Organization Robotics and Perception
Hosts Reiter Rudolf
Topics Information, Computing and Communication Sciences , Engineering and Technology
Meta-model-based-RL for adaptive flight control
This research project aims to develop and evaluate a meta model-based reinforcement learning (RL) framework for addressing variable dynamics in flight control.
Keywords
Model-based Reinforcement Learning, Meta Learning, Drones, Robotics
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Master Thesis
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Published since: 2025-04-10 , Earliest start: 2025-05-01 , Latest end: 2026-04-30
Organization Robotics and Perception
Hosts Geles Ismail
Topics Information, Computing and Communication Sciences , Engineering and Technology
Language-guided Drone Control
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: 2025-04-10 , Earliest start: 2025-05-01 , Latest end: 2026-02-28
Organization Robotics and Perception
Hosts Geles Ismail
Topics Information, Computing and Communication Sciences , Engineering and Technology
Smart Microcapsules for Biomedical Advances
This Master's thesis/semester project focuses on the microfluidic fabrication of microcapsules with multi-environmental responsiveness. The aim is to develop microcapsule-based microrobots capable of adapting to various environmental cues. We envision that these microrobots will be used for complex tasks in biomedical applications.
Keywords
Microfluidics, Microcapsules, Microrobotics, Responsive Polymers, Biomedical Engineering
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Semester Project , Internship , Master Thesis , Student Assistant / HiWi , ETH Zurich (ETHZ)
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Published since: 2025-04-08 , Earliest start: 2025-07-01
Organization Multiscale Robotics Lab
Hosts Hu Minghan
Topics Medical and Health Sciences , Engineering and Technology , Chemistry
Low-Voltage Soft Actuators for Developing Untethered Robotic Systems
We are building the next generation of HALVE (Hydraulically Amplified Low-Voltage Electrostatic) actuators which are flexible, pouch-based electrostatic actuators that operate at voltages 5–10× lower than traditional soft electrostatic systems. You will help us explore novel actuator geometries, ultra-thin functional layers, and new fabrication techniques to unlock scalable, energy-efficient soft robotic systems.
Keywords
soft robotics, low-voltage actuation, dielectric elastomers, electrostatic actuators, fabrication, PVDF-TrFE-CTFE, vapor deposition, CNC sealing, mechatronics, materials science
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-04-07 , Earliest start: 2025-04-14 , Latest end: 2026-01-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa
Organization Soft Robotics Lab
Hosts Albayrak Deniz , Hinchet Ronan
Topics Engineering and Technology
Combined Muscle and Nerve Tissue Engineering
Engineered muscle tissues have applications in regenerative medicine, drug testing, and understanding motion. A key challenge is restoring neuromuscular communication, especially in treating volumetric muscle loss (VML). This project aims to create functional neuromuscular constructs with biomimetic innervation. Scaffolds will be made using electrospun fibers, conductive materials, and drug-loaded graphene. Muscle and nerve cells derived from iPSCs will be seeded into these scaffolds. Constructs will be tested for motion, drug response, and integration in bio-hybrid robotic systems. The platform will advance muscle-nerve regeneration, drug testing, and bio-hybrid robotics.
Keywords
Tissue engineering, innervation, neural tissue, nerve, muscle tissue, scaffold, iPSCs, muscle cells, bioprinting, biofabrication, biohybrid robotics, soft robotics, 3D printing, biomaterials, electrical stimulation, actuation.
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Semester Project , Master Thesis
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Published since: 2025-04-06 , Earliest start: 2025-04-06 , Latest end: 2025-09-30
Organization Soft Robotics Lab
Hosts Filippi Miriam
Topics Medical and Health Sciences , Engineering and Technology , Biology
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
Develop Dexterous Humanoid Robotic Hands
Design and build dexterous human-like robotic hands with us at the Soft Robotics Lab and the ETH spin-off mimic. We will explore different possibilities of developing design features and sub-systems. The developed features shall be integrated into a fully functional robotic hand and applied to solve practical manipulation challenges.
Keywords
humanoid, robotics, hand, dexterity, soft robotics, actuation, prototyping, modeling and control, mechatronics, biomimetic, design, 3D printing, silicone casting, electronics, machine learning, control
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Semester Project , Master Thesis
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Published since: 2025-03-24 , Earliest start: 2025-01-01 , Latest end: 2026-03-31
Organization Soft Robotics Lab
Hosts Weirich Stefan
Topics Engineering and Technology
Advancing Space Navigation and Landing with Event-Based Camera in collaboration with the European Space Agency
In this project, you will investigate the use of event-based cameras for vision-based landing on celestial bodies such as Mars or the Moon.
Keywords
event-based camera, vision-based navigation
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Master Thesis
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Published since: 2025-03-19 , Earliest start: 2025-03-23 , Latest end: 2025-12-31
Applications limited to University of Zurich , ETH Zurich
Organization Robotics and Perception
Hosts Cannici Marco , Pellerito Roberto
Topics Engineering and Technology
Time-continuous Facial Motion Capture Using Event Cameras
Traditional facial motion capture systems, including marker-based methods and multi-camera rigs, often struggle to capture fine details such as micro-expressions and subtle wrinkles. While learning-based techniques using monocular RGB images have improved tracking fidelity, their temporal resolution remains limited by conventional camera frame rates. Event-based cameras present a compelling solution, offering superior temporal resolution without the cost and complexity of high-speed RGB cameras. This project explores the potential of event-based cameras to enhance facial motion tracking, enabling the precise capture of subtle facial dynamics over time.
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-03-19 , Earliest start: 2025-03-23 , Latest end: 2025-12-31
Organization Robotics and Perception
Hosts Pellerito Roberto
Topics Engineering and Technology
Intelligent Micromachines Made from Droplet-Based Factory
We invite applications for a Master's thesis / semester project that focuses on the fabrication of microrobots with custom shapes. Using our developed droplet printing technique, this project will explore how different microrobot shapes, created by different magnetic fields and materials, influence their control behaviors in blood vessels. This research aims to advance biomedical technologies, particularly in targeted drug delivery and minimally invasive procedures.
Keywords
Microrobotics, 4D Printing, Soft Materials, Biomedical Devices
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Semester Project , Master Thesis , Student Assistant / HiWi , ETH Zurich (ETHZ)
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Published since: 2025-03-18 , Earliest start: 2025-06-02
Organization Multiscale Robotics Lab
Hosts Hu Minghan
Topics Engineering and Technology , Chemistry
Better Scaling Laws for Neuromorphic Systems
This project explores and extends the novel "deep state-space models" framework by leveraging their transfer function representations.
Keywords
deep learning, state space models, transfer function, parameterizations, S4 model, Fourier Transform, Convolution, Neuromorphic Systems, Neuromorphic, Sequence Modeling, Event-based Vision
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Semester Project , Master Thesis
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Published since: 2025-03-18
Applications limited to University of Zurich , ETH Zurich
Organization Robotics and Perception
Hosts Zubic Nikola
Topics Mathematical Sciences , Information, Computing and Communication Sciences , 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: 2025-07-01 , 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
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
Development of intelligent lab-on-a-chip devices for high-throughput cell manipulation and microrobot production
Microfluidic devices can be employed in biological research as lab-on-a-chip (LoC) and organ-on-a-chip (OoC) systems. These platforms enable precise in-situ cell manipulation within a highly controlled environment. In the project, we aim to develop an intelligent LoC/OoC device featuring a flexible smart “switch”, for massive production of biohybrid microrobots and high-throughput cell manipulation and drug testing.
Keywords
Lab-on-a-chip, organ-on-a-chip, magnetic microrobot, cell manipulation
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-03-05 , Earliest start: 2025-03-10 , Latest end: 2025-12-31
Applications limited to ETH Zurich , Empa , EPFL - Ecole Polytechnique Fédérale de Lausanne , 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 , Zurich University of Applied Sciences , Zurich University of the Arts , Hochschulmedizin Zürich , Lucerne University of Applied Sciences and Arts
Organization Multiscale Robotics Lab
Hosts Zhu Jiawei
Topics Engineering and Technology , Biology
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
Electrical Flow-Based Graph Embeddings for Event-based Vision and other downstream tasks
This project explores a novel approach to graph embeddings using electrical flow computations.
Keywords
graph neural networks, graph representation learning, spectral graph theory, network analysis, electrical flow, event-based vision, low-dimensional graph representations
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Master Thesis
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Published since: 2025-03-04 , Earliest start: 2024-09-02
Applications limited to University of Zurich , ETH Zurich
Organization Robotics and Perception
Hosts Zubic Nikola
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Behavioural and Cognitive Sciences
Leveraging Long Sequence Modeling for Drone Racing
Study the application of Long Sequence Modeling techniques within Reinforcement Learning (RL) to improve autonomous drone racing capabilities.
Keywords
long sequence modeling, state-space models, convolutional neural networks, CNNs, recurrent neural networks, RNNs, sequence dynamics, dynamical systems, reinforcement learning, RL, optimal control, drone racing, machine learning, autonomous navigation
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Master Thesis
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Published since: 2025-03-04
Applications limited to ETH Zurich , University of Zurich
Organization Robotics and Perception
Hosts Zubic Nikola
Topics Information, Computing and Communication Sciences , Engineering and Technology
Neural Architecture Knowledge Transfer for Event-based Vision
Perform knowledge distillation from Transformers to more energy-efficient neural network architectures for Event-based Vision.
Keywords
deep neural networks, knowledge transfer, knowledge distillation, event cameras, event-based vision, sequence modeling, transformers, low-energy vision
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Master Thesis
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Published since: 2025-03-04
Applications limited to ETH Zurich , University of Zurich
Organization Robotics and Perception
Hosts Zubic Nikola
Topics Information, Computing and Communication Sciences , Engineering and Technology
Deep Learning of Residual Physics For Soft Robot Simulation
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: 2025-03-02 , Earliest start: 2025-03-01 , Latest end: 2026-03-01
Organization Soft Robotics Lab
Hosts Michelis Mike , Katzschmann Robert, Prof. Dr.
Topics Information, Computing and Communication Sciences , 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
Acoustic Standing Waves for Particle Manipulation in Air
Acoustic standing waves can be used to manipulate physical objects in both gas and liquid environments. This project investigates their effects on particle flow and selectivity in air, considering various particle sizes and weights. Through modeling, simulations, and experimental validation, we aim to characterize the selectivity of these waves and develop a compact driver circuit for practical implementation. The student will work closely with Honeywell engineers on test setups, electronic designs, and prototyping. A successful outcome may lead to a subsequent R&D phase or PhD project in a collaboration with Honeywellto further develop these findings.
Keywords
Acoustics, Standing Waves, Particle Manipulation, Flow Control, Electronics
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Master Thesis
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Published since: 2025-02-25 , Earliest start: 2025-02-25 , Latest end: 2025-09-30
Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)
Hosts Medany Mahmoud
Topics Engineering and Technology , Physics
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
Piezoelectric Atomization: Optimizing Liquid and Gel Dispersion
Piezoelectric elements are widely used for particle manipulation and atomization, with applications in humidification, cooling, and medical aerosol generation. However, temperature and environmental factors can impact the efficiency of vaporization and the properties of the generated droplets. Additionally, the heat generated by piezo elements affects particle size and flux, requiring careful control. This project will investigate the effect of piezoelectric elements on liquid and gel atomization, optimizing power consumption, repeatability, and calibration. A proof-of-concept demonstrator will be developed to study these parameters under controlled conditions. A successful outcome may lead to a subsequent R&D phase or PhD project in collaboration with Honeywell to further develop these findings
Keywords
Piezoelectric Atomization, Liquid/Gel Dispersion, Energy Efficiency, Particle Size Control, Low-Power Electronics
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Master Thesis
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Published since: 2025-02-25 , Earliest start: 2025-02-25 , Latest end: 2025-09-30
Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)
Hosts Medany Mahmoud
Topics Engineering and Technology , Physics
Generating Realistic Event Camera Data with Generative AI
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: 2025-02-10 , Earliest start: 2024-12-12
Organization Robotics and Perception
Hosts Messikommer Nico
Topics Information, Computing and Communication Sciences
Enhancing Robotic Motor Policies with Event Cameras
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.
Keywords
Computer Vision, Event Cameras, Robotics, Unsupervised Domain Adaption
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Semester Project , Master Thesis
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Published since: 2025-02-10 , Earliest start: 2024-12-12
Organization Robotics and Perception
Hosts Messikommer Nico
Topics Information, Computing and Communication 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
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
Untethered ultrafast-rotating spiral microrobot for physical thrombolysis
The ability to manipulate micro-scale objects with precision is a growing field in biomedical engineering, particularly in the context of treating thrombotic conditions. Thrombolysis, the process of dissolving blood clots, remains a significant challenge in medical treatment, with current techniques often limited by their invasiveness and effectiveness. Recent advancements have explored the use of microrobots for targeted thrombolysis, leveraging their ability to maneuver in complex biological environments to enhance clot dissolution and drug delivery. Rotation plays a crucial role in various natural processes, including feeding and locomotion, demonstrating its effectiveness in achieving complex interactions with the environment. However, achieving ultrafast rotation in artificial microrobots presents significant engineering challenges. Traditional methods of inducing rotation, such as acoustic manipulation, have shown promise but are often constrained by limitations in rotational speed and control precision. These constraints hinder the microrobot's ability to effectively engage with functions. In response to these challenges, we introduce an innovative solution: an untethered ultrafast-rotating spiral microrobot designed for physical thrombolysis. This microrobot employs a symmetric spiral structure that generates a consistent torque while maintaining a zero net force, allowing for sustained high-speed rotation. The unique design of the spiral structure ensures efficient rotational motion, overcoming previous limitations in rotation speed. A key feature of our microrobot is its sharp-tip design, which enhances its ability to penetrate and mechanically disrupt thrombi. This mechanical drilling action facilitates the breakdown of clots, making thrombolysis more effective. Additionally, the microrobot incorporates a drug-holding cavity, enabling it to deliver therapeutic agents directly to the site of the thrombus. This dual functionality—mechanical disruption combined with targeted drug delivery—promises a more efficient approach to thrombolysis. This ultrafast-rotating microrobot represents a significant advancement in microrobot design and its application in medical treatments.
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Keywords: Rotation; acoustic microrobot; thrombolysis
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Master Thesis
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Published since: 2025-02-05 , Earliest start: 2025-02-06 , Latest end: 2025-09-30
Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)
Hosts Deng Yong
Topics Engineering and Technology
Designing Freeform Trajectories through Acoustic Streaming and Artificial Intelligence
The manipulation of materials and fluids through acoustic streaming has emerged as a powerful technique with applications in manufacturing and biomedical engineering. This method utilizes sound waves to control the movement of particles within a fluid, offering precise and non-invasive manipulation. However, achieving freeform path manipulation—guiding materials along complex, non-linear trajectories—remains a significant challenge due to difficulties in controlling the influence range and vortex dynamics of acoustic streaming. Traditional methods often struggle with maintaining precision and stability along intricate paths, as the non-uniform distribution of acoustic forces complicates consistent directionality. Artificial Intelligence (AI) presents a promising solution, enabling real-time control and optimization of these systems. By integrating AI with acoustic streaming, algorithms can analyze and predict the interactions between acoustic forces and fluid dynamics, allowing for dynamic adjustments that enhance accuracy. In this thesis, we propose addressing these challenges by implementing a pillar array of acoustic actuators coupled with AI-driven control systems. The pillar array will generate and modulate acoustic streaming fields, while AI will optimize and automate their control in real time. This integration aims to improve the precision of freeform path manipulation, facilitating the creation of complex patterns that are otherwise difficult to achieve, thereby expanding the possibilities for material manipulation across various applications.
Keywords
Freeform path; Manipulation; Ultrasound; pillar array; AI
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-02-05 , Earliest start: 2025-02-06 , Latest end: 2025-09-30
Organization Acoustic Robotics for Life Sciences and Healthcare (ARSL)
Hosts Deng Yong
Topics Engineering and Technology
Computational Modeling of Muscle Dynamics for Biohybrid Robots
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.
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Biohybrid Robotics, Computational Models, Soft Body Simulation, Finite Element Method (FEM), Muscle Dynamics, Soft Robotics
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-02-04 , Earliest start: 2025-02-01 , Latest end: 2026-02-01
Organization Soft Robotics Lab
Hosts Mekkattu Manuel , Katzschmann Robert, Prof. Dr.
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology , Biology , Physics
GPU Acceleration of Soft Robot Modeling: Enhancing Performance with CUDA
We are enhancing soft robot modeling by developing a GPU-accelerated version of our FEM-based framework using CUDA. This research focuses on optimizing parallel computations to significantly speed up simulations, enabling larger problem sizes and real-time control. By improving computational efficiency, we aim to advance soft robotics research and facilitate more detailed, dynamic simulations.
Keywords
Soft Body Simulation, high-performance computing, GPU programming, Parallel Computing, Finite Element Method (FEM), Multiphysics Simulation
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-02-04 , Earliest start: 2025-02-01 , Latest end: 2026-02-01
Organization Soft Robotics Lab
Hosts Katzschmann Robert, Prof. Dr. , Mekkattu Manuel
Topics Information, Computing and Communication Sciences , Engineering and Technology
Advancing Soft Robot Modeling: Integrating Physics, Optimization, and Control
We are advancing soft robot simulation with FEM and energy-based methods to model complex, adaptive behaviors. This research entails developing the framework to support diverse designs, integrate new physics models, and optimize performance, enabling enhanced control and real-world applications of soft robots.
Keywords
Soft Robotics, Finite Element Method (FEM), Physical Modeling, Benchmarking, Optimization, Multiphysics Simulation, Sim-to-Real
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-02-04 , Earliest start: 2025-02-01 , Latest end: 2026-02-01
Organization Soft Robotics Lab
Hosts Mekkattu Manuel , Katzschmann Robert, Prof. Dr.
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.
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Keywords: Reinforcement learning, task planning
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Semester Project , Master Thesis
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Published since: 2025-02-03 , Earliest start: 2025-07-01 , 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
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.
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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: 2025-07-01 , 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: 2025-07-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
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