Projects and Theses
Visual Object Reconstruction and Generalized Collision-aware Manipulation using Reinforcement Learning
We propose to develop a neural motion planning method that incorporates 3D-reconstructed geometry of in-hand objects into a reinforcement learning policy for collision-aware manipulation. This enables generalization across novel tools and objects with precise, geometry-driven motion in both stationary and mobile robotic setups.
Keywords
Neural motion planning, 3D reconstruction, reinforcement learning, mobile manipulation
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-06-30 , Earliest start: 2025-08-31 , Latest end: 2026-07-31
Organization Robotic Systems Lab
Hosts Portela Tifanny , Zurbrügg René
Topics Information, Computing and Communication Sciences
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: 2025-06-30 , 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: 2025-06-30 , 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
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
Keywords: machine learning, deep learning, artificial intelligence, blood pressure, pulse transit time, vascular imaging, Doppler
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Semester Project , Internship , Master Thesis
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Published since: 2025-06-28 , Earliest start: 2025-07-01 , Latest end: 2026-07-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Otesteanu Corin, Dr
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 , Internship , Master Thesis
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Published since: 2025-06-28 , Earliest start: 2025-08-01 , Latest end: 2026-08-01
Organization Biomedical and Mobile Health Technology Lab
Hosts Otesteanu Corin, Dr
Topics Information, Computing and Communication Sciences , Engineering and Technology
Artificial intelligence for anxiety level classification using data from wearable devices
The aim of this project is to study the feasibility of using wearable devices for anxiety detection using machine learning models. By creating a robust framework for continuous monitoring and early assessment, it has the potential to meaningfully impact the users wellbeing.
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Keywords: wearable technology, anxiety monitoring, health tracking, machine learning, artificial intelligence
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Semester Project , Internship , Master Thesis
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Published since: 2025-06-28 , Earliest start: 2025-08-01 , Latest end: 2026-07-01
Organization Biomedical and Mobile Health Technology Lab
Hosts Otesteanu Corin, Dr
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Master Thesis: Adaptive Motivational Treadmill - Personalized Biofeedback and Avatar-Guided Gait Training
This Master’s thesis project focuses on enhancing gait rehabilitation by integrating real-time heart rate and respiratory monitoring, intelligent treadmill control, and AI-driven avatar feedback. Physiological data are processed by a LLM to generate personalized verbal encouragement, while an AI-based controller adjusts treadmill speed to maintain clinician-defined heart rate zones and adapt to patient performance. The project aims to deliver personalized, engaging, and adaptive rehabilitation and involves system integration, algorithm development, and experimental validation.
Keywords
rehabilitation, robotics, model-based control, LLM integration
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-06-26 , Earliest start: 2025-08-01 , Latest end: 2026-03-31
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr.
Topics Medical and Health Sciences , Engineering and Technology
Development of Wireless Ion Sensing Platforms using Metamaterials and Soft Biointerfaces
This project explores the design and realization of a flexible, wireless ion-sensing patch by integrating resonant metamaterial structures with bio-interfacing soft materials. The system is intended for noninvasive detection of physiologically relevant ions from skin-interfaced fluids using passive sensing mechanisms
Keywords
flexible electronics, metamaterials, wireless biosensors, resonant sensors, skin-compatible interfaces
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-06-26 , Earliest start: 2025-08-01 , Latest end: 2025-12-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Zada Muhammad
Topics Engineering and Technology
3D reconstruction with open-vocabulary reconstruction priors
Push the limits of arbitrary online video reconstruction by combining the most recent, prior-supported real-time Simultaneous Localization And Mapping (SLAM) methods with automatic backend regularization techniques.
Keywords
Structure from Motion Visual SLAM
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-06-26 , Earliest start: 2025-07-01
Organization Robotic Systems Lab
Hosts Kneip Laurent
Topics Engineering and Technology
Learning Manipulation beyond Single End-Effector
Robots, like humans, should be able to use different parts of their morphology (base, elbow, hips, feet) for interaction. This project focuses on learning multi-modal interactions from demonstrations for mobile manipulators.
Keywords
machine learning, manipulation, robotics
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Semester Project , Master Thesis
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Published since: 2025-06-25 , Earliest start: 2024-01-08
Organization Robotic Systems Lab
Hosts Mittal Mayank
Topics Information, Computing and Communication Sciences , Behavioural and Cognitive Sciences
Wearable 2D Capacitive Auxetic Structures for Motion Monitoring
The aim of this project is to develop a single sensor capable of measuring both unidirectional strain and bending angle.
Keywords
wearable, flexible electronics, 3D printing, capacitive strain sensors
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-06-24 , Earliest start: 2025-08-01 , Latest end: 2026-12-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Kateb Pierre
Topics Engineering and Technology
CI/CD Automation and Testing for Embedded Systems
Design and implement a robust CI/CD pipeline for our embedded software development.
Keywords
firmware, embedded, github, cicd, workflow
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Internship , Lab Practice
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Published since: 2025-06-23
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius
Topics Information, Computing and Communication Sciences
RA position: Combining Human And Robot Data To Train Manipulation Policies
Data collection by humans for training robotic policies is traditionally a strenuous task. It requires the recorder to teleoperate the robot, commonly leading to a slow task execution due to the indirect and unfamiliar control mechanism, and the need to record task demonstrations multiple times due to control imperfections. The work EgoMimic [1] introduces a framework intelligently combining human-only task demonstrations without robotic hardware with traditional human-robot ones, greatly reducing the required recording time due to the high proportion of quick-to-record human-only demos. In this project, four top research labs (from Georgia Tech, Stanford, UCSD, and ETH) across the world join efforts to test various challenging hypotheses, such as scaling laws in robotic learning, cross-embodiment generalisation, etc.
Keywords
egocentric vision, robotics, robotic manipulation
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Student Assistant / HiWi , ETH Zurich (ETHZ)
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Published since: 2025-06-23 , Earliest start: 2025-06-20
Organization Computer Vision and Geometry Group
Hosts Zurbrügg René , Zurbrügg René , Wang Xi , Chen Jiaqi , Gavryushin Alexey
Topics Information, Computing and Communication Sciences , Engineering and Technology
Flash Storage and USB Communication for Embedded Systems
This project targets performance and feature enhancements to a firmware module that manages data transfers from flash memory to a host PC over USB (CDC/ACM). The goal is to improve system reliability, throughput, and communication efficiency for data logging or diagnostic use cases.
Keywords
embedded, c programming, firmware, flash memory, usb
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Internship , Lab Practice
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Published since: 2025-06-23
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius
Topics Information, Computing and Communication Sciences , Engineering and Technology
Doxygen Documentation Pipeline for Embedded Firmware
You will help improve the usability and maintainability of Skaaltec’s embedded firmware codebase by integrating automated documentation generation using Doxygen.
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Published since: 2025-06-23
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius
Topics Information, Computing and Communication Sciences
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
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
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
Pre-clinical mechanical evaluation of a novel spinal implant
Lower back pain is one of the most prevalent health issues in Switzerland, with severe socio-economic consequences and a leading cause of reduced work performance. Approximately 20% of spinal fusion surgeries performed using off-the-shelf implants result in the surgical outcome being compromised post-operatively, often requiring one or more revision surgeries to address the associated pain. The Laboratory of Orthopedic Technology has recently developed a novel spinal implant using topology optimization, which is currently undergoing a feasibility study for clinical applications. We are seeking a master’s student who is passionate about medical devices and mechanical design and testing to join us for a master thesis. In this role, you will gain insight into the spinal surgery process, receive input from surgeons, and contribute to the mechanical testing of the implant on human cadaveric spine. Objectives: • Perform the CT scan on human cadaveric vertebrae • Evaluate the influence of implant placement/location variability • Mechanical testing on the implant and failure mode analysis • Develop surgical tools if needed • Write related SOPs and testing report Your Profile: • Hands-on and detail-oriented, need to work with human cadaveric bones. • Experience with SolidWorks or Fusion 360, as well as Python or Matlab. • Need to have Hepatitis B Vaccine to be able to work in BSL 2 level labs
Keywords
implant, medical device, mechanical testing, clinical application
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Master Thesis
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Published since: 2025-06-10 , Earliest start: 2025-06-23 , Latest end: 2026-01-30
Organization Bone Pathologies and Treatment
Hosts Du Xiaoyu
Topics Medical and Health 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
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
Internship or Master Thesis Opportunity in Pupil-Based Biofeedback data analysis
Are you passionate about neuroinformatics, data science, or biosignal processing and looking for an exciting internship/master thesis opportunity at ETH Zurich and SFISM? Join our interdisciplinary team in an SNF BRIDGE project that explores how pupil-based biofeedback training can help athletes to regulate their state of arousal. Arousal regulation is critical for cognitive performance and well-being. Our research focuses on using the pupil as a real-time physiological marker of central arousal, linked to the locus coeruleus-noradrenaline (LC-NA) system. We have developed an innovative pupil-based biofeedback system integrated into commercially available VR headsets with eye-tracking capabilities. Our goal is to investigate how prolonged pupil biofeedback training influences self-regulation of arousal at rest and during cognitive tasks, and how factors such as demographics and personality traits shape its effectiveness. The project will be jointly supervised by the Neural Control of Movement lab at ETH Zurich and the Sports Psychology group at SFISM. A substantial part of the research activities will take place in Magglingen.
Keywords
pupillometry, time series analysis, neuroscience, biofeedback, neurofeedback
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Semester Project , Internship , Master Thesis
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Published since: 2025-05-27 , Earliest start: 2025-06-15 , Latest end: 2026-03-01
Organization Neural Control of Movement Lab
Hosts Weijs Marieke
Topics Medical and Health Sciences
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
HandoverNarrate: Language-Guided Task-Aware Motion Planning for Handovers with Legged Manipulators
This project addresses the challenge of task-oriented human-robot handovers, where a robot must transfer objects in a manner that directly facilitates the human’s next action. In our prior work, we demonstrated that robots can present objects appropriately for immediate human use by leveraging large language models (LLMs) to reason about task context. However, integrating task-specific physical constraints—such as ensuring a full mug remains upright during transport—into the motion planning process remains unsolved. In this project, we aim to extend our existing motion planning framework for legged manipulators by incorporating such constraints. We propose using LLMs to dynamically generate task-aware constraint formulations based on high-level task descriptions and object states. These constraints will then be used to adjust the cost function of the model predictive controller in real time, enabling more context-sensitive and physically appropriate handovers.
Keywords
language-guided motion planning, legged robotics, human-robot collaboration
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Semester Project , Bachelor Thesis
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Published since: 2025-05-21
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Tulbure Andreea
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
Design and development of a novel printing approach
3D printing has revolutionized the way objects are designed and fabricated across a wide range of industries—from aerospace and automotive to healthcare and consumer products. It enables rapid prototyping, complex geometries, customized solutions, and recently bioprinting of living tissues that are difficult or impossible to achieve with traditional manufacturing methods. Every 3D printing method has certain drawbacks, often related to resolution, material compatibility, speed, or scalability. The ongoing search for new approaches aims to overcome these challenges and expand the potential of the technology. We have developed and demonstrated a proof of concept for a novel printing approach, and are now seeking to advance it into a fully functional prototype.
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Semester Project , Master Thesis
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Published since: 2025-05-15 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Zenobi-Wong Group / Tissue Engineering and Biofabrication
Hosts Janiak Jakub
Topics Engineering and Technology
Stanford – UC Berkeley Collaboration: Learning Progress Driven Reinforcement Learning for ANYmal
TLDR: Improving navigation capabilities of ANYmal - RL is simulation - optimizing learning progress.
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Semester Project , Master Thesis
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Published since: 2025-05-14 , Earliest start: 2025-05-14 , Latest end: 2025-08-31
Applications limited to EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , University of Zurich
Organization Robotic Systems Lab
Hosts Frey Jonas
Topics Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences
Multi-Critic Reinforcement Learning for Whole-Body Control of Bimanual Legged Manipulator
Recent work in legged robotics shows the promise of unified control strategies for whole-body control. Portela et al. (2024) demonstrated force control without force sensors, enabling compliant manipulation through body coordination. In another study, they achieved accurate end-effector tracking using whole-body RL with terrain-aware sampling. Fu et al. (2023) showed that unified policies can dynamically handle both movement and manipulation in quadruped robots by training with two critics: one for arms, and one for legs, and then gradually combining them. In this project, you will investigate reinforcement learning for whole body control of a bimanual legged manipulator. You will implement a baseline single-critic whole body controller for the system. You will then investigate different multi-critic approaches and their effects on the training and final performance of the whole-body controller. References: Learning Force Control for Legged Manipulation, Portela et al., 2024 Whole-Body End-Effector Pose Tracking, Portela et al., 2024 Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion, Fu et al., 2023
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Semester Project , Master Thesis
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Published since: 2025-05-09 , Earliest start: 2025-05-11 , Latest end: 2025-12-31
Applications limited to ETH Zurich , [nothing]
Organization Robotic Systems Lab
Hosts Fischer Oliver , Elanjimattathil Aravind
Topics Engineering and Technology
Diffusing Time Series in the Wavelet Domain
Diffusion models (DDPMs) have revolutionised generative modelling, surpassing GANs in images, advancing audio synthesis, and enabling de-novo protein design. Yet progress on time series lags behind early adversarial work. Recent studies highlight the benefits of spectral biases - FourierFlow and frequency-domain DDPMs. In parallel, diffusion in the wavelet domain has emerged for images, offering a multi-resolution view well-suited to non-stationary signals. Wavelets capture localised, scale-dependent features, making them attractive for domains from finance to climate and biomedical data such as ECGs. This project proposes the first DDPM framework operating directly in the wavelet domain for time series, aiming to improve generalisation, interpretability, and robustness across diverse sequential tasks.
Keywords
Diffusion Models, Time Series, Wavelet Domain
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-05-09
Organization Medical Data Science
Hosts Ruiperez-Campillo Samuel
Topics Mathematical Sciences , Information, Computing and Communication Sciences
Visual Language Models for Long-Term Planning
This project uses Visual Language Models (VLMs) for high-level planning and supervision in construction tasks, enabling task prioritization, dynamic adaptation, and multi-robot collaboration for excavation and site management. prioritization, dynamic adaptation, and multi-robot collaboration for excavation and site management
Keywords
Visual Language Models, Long-term planning, Robotics
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Semester Project , Master Thesis
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Published since: 2025-05-07 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
AI Agents for Excavation Planning
Recent advancements in AI, particularly with models like Claude 3.7 Sonnet, have showcased enhanced reasoning capabilities. This project aims to harness such models for excavation planning tasks, drawing parallels from complex automation scenarios in games like Factorio. We will explore the potential of these AI agents to plan and optimize excavation processes, transitioning from simulated environments to real-world applications with our excavator robot.
Keywords
GPT, Large Language Models, Robotics, Deep Learning, Reinforcement Learning
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Semester Project , Master Thesis
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Published since: 2025-05-07 , Earliest start: 2025-07-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Engineering and Technology
BEV meets Semantic traversability
Enable Birds-Eye-View perception on autonomous mobile robots for human-like navigation.
Keywords
Semantic Traversability, Birds-Eye-View, Localization, SLAM, Object Detection
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-04-29 , Earliest start: 2025-01-15 , Latest end: 2025-10-31
Organization Robotic Systems Lab
Hosts Gawel Abel
Topics Information, Computing and Communication Sciences , Engineering and Technology
Scene graphs for robot navigation and reasoning
Elevate semantic scene graphs to a new level and perform semantically-guided navigation and interaction with real robots at The AI Institute.
Keywords
Scene graphs, SLAM, Navigation, Spacial Reasoning, 3D reconstruction, Semantics
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-04-29 , Earliest start: 2025-01-15 , Latest end: 2025-10-31
Organization Robotic Systems Lab
Hosts Gawel Abel , Kneip Laurent
Topics Information, Computing and Communication Sciences , Engineering and Technology
Modelling and Optimizing the Power Budget of a Bridge-Mounted Camera System for River Waste Monitoring
In this thesis, you will contribute to the Autonomous River Cleanup (ARC) by helping develop SARA, a bridge-mounted, camera-based system for monitoring river waste. Your focus will be on modeling the system’s power dynamics to determine the ideal battery and solar panel size, and balancing runtime throughout the day with overall the system size and weight. If time allows, you will also validate your findings with tests on the real hardware.
Keywords
system modelling, power electronics, simulations
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Semester Project , Bachelor Thesis
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Published since: 2025-04-27 , Earliest start: 2025-05-05 , Latest end: 2025-09-30
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Elbir Emre
Topics Engineering and Technology
Domain Adaptation Techniques for Vision Algorithms on a Smartphone for River Waste Monitoring
In this thesis, you will work on SARA, a bridge-mounted, smartphone-based system for detecting and monitoring river waste. The focus will be on selecting lightweight detection and classification models suitable for smartphones and exploring domain adaptation techniques to improve performance across different locations with minimal retraining. Your work will build on previous research at ARC and current literature to develop solutions that balance model robustness and computational efficiency.
Keywords
machine learning, computer vision, domain adaptation techniques
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Published since: 2025-04-27 , Earliest start: 2025-05-05 , Latest end: 2025-09-30
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Elbir Emre
Topics Engineering and Technology
Optimal Robot Configuration for Autonomous Waste Sorting in Confined Spaces
In this thesis, you will contribute to the Autonomous River Cleanup (ARC) by helping improve MARC, our robotic platform for autonomous waste sorting. Your work will focus on optimizing the robot arm configuration by simulating different base locations and degrees of freedom to achieve faster and more efficient pick-and-place movements in a confined space. You will build on our existing simulation environment to model and evaluate various setups.
Keywords
modelling and simulations, robotics, robot dynamics
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Semester Project
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Published since: 2025-04-27 , Earliest start: 2025-05-05 , Latest end: 2025-09-30
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Elbir Emre
Topics Engineering and Technology
Thermal Protection of a Bridge-Mounted Camera System for River Waste Monitoring
The Autonomous River Cleanup (ARC) is developing SARA, the next iteration of a bridge-mounted, camera-based system to detect and measure riverine waste. Smartphones offer a compact, affordable, and powerful core for year-round monitoring but are vulnerable to shutdowns from extreme heat in summer and cold in winter. This thesis focuses on assessing these thermal challenges and designing protective solutions to ensure reliable, continuous operation.
Keywords
thermodynamics, heat transfer, testing
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Semester Project , Bachelor Thesis
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Published since: 2025-04-27 , Earliest start: 2025-05-05 , Latest end: 2025-09-30
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Elbir Emre
Topics Engineering and Technology
Gaussian Avatar Reconstruction from Single Image
In this project, you are going to work with a state-of-the-art deep learning approach and generative models for building an efficient system to directly reconstruct a 3D animatable avatar from a single image. Feel free to contact me for more details.
Keywords
3D Gaussian Avatar, Diffusion Model, 3D from single image
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-04-23 , Earliest start: 2025-05-01 , Latest end: 2025-12-15
Organization Advanced Interactive Technologies
Hosts Dong Zijian
Topics Information, Computing and Communication Sciences
Tissue Engineering Approaches to Study Tendon Injury, Disease, and Therapy
Join a dynamic research team at the intersection of biomechanics, tissue engineering, and cell biology. This project offers hands-on training in state-of-the-art methods to investigate how tendon tissue responds to injury, disease processes, and mechanical stimulation during exercise-based therapy.
Keywords
Tendon biology, tissue engineering, mechanobiology, cell culture, microscopy, regenerative medicine, exercise therapy, inflammation, ECM remodeling
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Master Thesis
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Published since: 2025-04-15 , Earliest start: 2025-05-01 , Latest end: 2026-12-31
Organization Snedeker Group / Laboratory for Orthopaedic Biomechanics
Hosts Snedeker Jess, Prof.
Topics Engineering and Technology
Research Assistant with data collection,cleaning,processing and programming skills
We are looking for a research assistant who is skilled at data collection, cleaning, matching and programming. Please see details in the attachment.
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Student Assistant / HiWi
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Published since: 2025-04-13 , Earliest start: 2025-04-13 , Latest end: 2025-05-15
Organization Chair of Strategic Management and Innovation
Hosts Liu Chang
Topics Information, Computing and Communication Sciences
Hardware Design Internship in Brain Imaging
Join us in revolutionizing brain imaging technologies and make it accessible for everyday use. Functional near-infrared spectroscopy (fNIRS) is an emerging technology that enables cost-effective and precise brain measurements, helping to improve neurotherapies and brain health.
Keywords
3D-printing, injection molding, design, brain imaging, neuro, wearables, health, startup
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Published since: 2025-04-09 , Earliest start: 2025-04-10 , Latest end: 2025-06-26
Organization Rehabilitation Engineering Lab
Hosts Wyser Dominik
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
PCB design for neuromorphic vision on nano-drones
In this project, we aim to develop a novel PCB integrating a powerful PULP chip, i.e., the GAP9, and event-based sensor, the Prophesee Genx320, and a RGB camera, the Himax HB0360 to enable multi modal AI-driven perception aboard nano-drones
Keywords
PCB design, nano-drones, robotics, event-cameras, neuromorphic computing, embedded devices, ultra-low-power
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-04-07 , Earliest start: 2025-04-07 , Latest end: 2025-12-01
Organization Digital Circuits and Systems (Benini)
Hosts Lamberti Lorenzo
Topics Engineering and Technology
Exploring the Mechanoregulation of Bone Regeneration
In over 100 years, the remarkable ability of bone to adapt to its mechanical environment has been a source of scientific fascination. Bone regeneration has been shown to be highly dependent on the mechanical environment at the fracture site. It has been demonstrated that mechanical stimuli can either accelerate or impede regeneration. Despite the fundamental importance of the mechanical environment in influencing bone regeneration, the molecular mechanisms underlying this phenomenon are complex and poorly understood.
Keywords
Bone, Mechanobiology, Spatial transcriptomics, Gene expression, Finite element modelling, Image processing
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2025-03-23 , Earliest start: 2024-11-01 , Latest end: 2025-08-31
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Mathavan Neashan
Topics Medical and Health Sciences , Engineering and Technology
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
Exploring the 3D Mineralization Behavior in Material-Induced Osteoinduction Through a Multiscale Micro-CT Imaging Approach
The project aims at investigating material-induced osteoinduction using the available mouse model of orthotopic or ectopic bone graft substitute (BGS) application. Through the 3D-3D registration of ex vivo and in vivo multiscale micro-CT images, crucial 3D mineralization behavior of the BGS can be investigated.
Keywords
Femur, Bone Graft Substitute, Critical Size Defect, Osteoinduction, in vivo, micro-CT, 3D-3D Image Registration, Image Analysis, Image Processing, Python, Computational
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-03-11 , Earliest start: 2025-04-01 , Latest end: 2026-01-31
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Lindenmann Sara
Topics Medical and Health Sciences , Engineering and Technology
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
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
Leveraging Human Motion Data from Videos for Humanoid Robot Motion Learning
The advancement in humanoid robotics has reached a stage where mimicking complex human motions with high accuracy is crucial for tasks ranging from entertainment to human-robot interaction in dynamic environments. Traditional approaches in motion learning, particularly for humanoid robots, rely heavily on motion capture (MoCap) data. However, acquiring large amounts of high-quality MoCap data is both expensive and logistically challenging. In contrast, video footage of human activities, such as sports events or dance performances, is widely available and offers an abundant source of motion data. Building on recent advancements in extracting and utilizing human motion from videos, such as the method proposed in WHAM (refer to the paper "Learning Physically Simulated Tennis Skills from Broadcast Videos"), this project aims to develop a system that extracts human motion from videos and applies it to teach a humanoid robot how to perform similar actions. The primary focus will be on extracting dynamic and expressive motions from videos, such as soccer player celebrations, and using these extracted motions as reference data for reinforcement learning (RL) and imitation learning on a humanoid robot.
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Master Thesis
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Published since: 2025-02-25
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Kaufmann Manuel , Li Chenhao , Li Chenhao , Kaufmann Manuel , Li Chenhao
Topics Engineering and Technology
Learning Agile Dodgeball Behaviors for Humanoid Robots
Agility and rapid decision-making are vital for humanoid robots to safely and effectively operate in dynamic, unstructured environments. In human contexts—whether in crowded spaces, industrial settings, or collaborative environments—robots must be capable of reacting to fast, unpredictable changes in their surroundings. This includes not only planned navigation around static obstacles but also rapid responses to dynamic threats such as falling objects, sudden human movements, or unexpected collisions. Developing such reactive capabilities in legged robots remains a significant challenge due to the complexity of real-time perception, decision-making under uncertainty, and balance control. Humanoid robots, with their human-like morphology, are uniquely positioned to navigate and interact with human-centered environments. However, achieving fast, dynamic responses—especially while maintaining postural stability—requires advanced control strategies that integrate perception, motion planning, and balance control within tight time constraints. The task of dodging fast-moving objects, such as balls, provides an ideal testbed for studying these capabilities. It encapsulates several core challenges: rapid object detection and trajectory prediction, real-time motion planning, dynamic stability maintenance, and reactive behavior under uncertainty. Moreover, it presents a simplified yet rich framework to investigate more general collision avoidance strategies that could later be extended to complex real-world interactions. In robotics, reactive motion planning for dynamic environments has been widely studied, but primarily in the context of wheeled robots or static obstacle fields. Classical approaches focus on precomputed motion plans or simple reactive strategies, often unsuitable for highly dynamic scenarios where split-second decisions are critical. In the domain of legged robotics, maintaining balance while executing rapid, evasive maneuvers remains a challenging problem. Previous work on dynamic locomotion has addressed agile behaviors like running, jumping, or turning (e.g., Hutter et al., 2016; Kim et al., 2019), but these movements are often planned in advance rather than triggered reactively. More recent efforts have leveraged reinforcement learning (RL) to enable robots to adapt to dynamic environments, demonstrating success in tasks such as obstacle avoidance, perturbation recovery, and agile locomotion (Peng et al., 2017; Hwangbo et al., 2019). However, many of these approaches still struggle with real-time constraints and robustness in high-speed, unpredictable scenarios. Perception-driven control in humanoids, particularly for tasks requiring fast reactions, has seen advances through sensor fusion, visual servoing, and predictive modeling. For example, integrating vision-based object tracking with dynamic motion planning has enabled robots to perform tasks like ball catching or blocking (Ishiguro et al., 2002; Behnke, 2004). Yet, dodging requires a fundamentally different approach: instead of converging toward an object (as in catching), the robot must predict and strategically avoid the object’s trajectory while maintaining balance—often in the presence of limited maneuvering time. Dodgeball-inspired robotics research has been explored in limited contexts, primarily using wheeled robots or simplified agents in simulations. Few studies have addressed the challenges of high-speed evasion combined with the complexities of humanoid balance and multi-joint coordination. This project aims to bridge that gap by developing learning-based methods that enable humanoid robots to reactively avoid fast-approaching objects in real time, while preserving stability and agility.
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Master Thesis
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Published since: 2025-02-25
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Engineering and Technology
Learning Real-time Human Motion Tracking on a Humanoid Robot
Humanoid robots, designed to mimic the structure and behavior of humans, have seen significant advancements in kinematics, dynamics, and control systems. Teleoperation of humanoid robots involves complex control strategies to manage bipedal locomotion, balance, and interaction with environments. Research in this area has focused on developing robots that can perform tasks in environments designed for humans, from simple object manipulation to navigating complex terrains. Reinforcement learning has emerged as a powerful method for enabling robots to learn from interactions with their environment, improving their performance over time without explicit programming for every possible scenario. In the context of humanoid robotics and teleoperation, RL can be used to optimize control policies, adapt to new tasks, and improve the efficiency and safety of human-robot interactions. Key challenges include the high dimensionality of the action space, the need for safe exploration, and the transfer of learned skills across different tasks and environments. Integrating human motion tracking with reinforcement learning on humanoid robots represents a cutting-edge area of research. This approach involves using human motion data as input to train RL models, enabling the robot to learn more natural and human-like movements. The goal is to develop systems that can not only replicate human actions in real-time but also adapt and improve their responses over time through learning. Challenges in this area include ensuring real-time performance, dealing with the variability of human motion, and maintaining stability and safety of the humanoid robot.
Keywords
real-time, humanoid, reinforcement learning, representation learning
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Master Thesis
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Published since: 2025-02-25
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Information, Computing and Communication Sciences
Loosely Guided Reinforcement Learning for Humanoid Parkour
Humanoid robots hold the promise of navigating complex, human-centric environments with agility and adaptability. However, training these robots to perform dynamic behaviors such as parkour—jumping, climbing, and traversing obstacles—remains a significant challenge due to the high-dimensional state and action spaces involved. Traditional Reinforcement Learning (RL) struggles in such settings, primarily due to sparse rewards and the extensive exploration needed for complex tasks. This project proposes a novel approach to address these challenges by incorporating loosely guided references into the RL process. Instead of relying solely on task-specific rewards or complex reward shaping, we introduce a simplified reference trajectory that serves as a guide during training. This trajectory, often limited to the robot's base movement, reduces the exploration burden without constraining the policy to strict tracking, allowing the emergence of diverse and adaptable behaviors. Reinforcement Learning has demonstrated remarkable success in training agents for tasks ranging from game playing to robotic manipulation. However, its application to high-dimensional, dynamic tasks like humanoid parkour is hindered by two primary challenges: Exploration Complexity: The vast state-action space of humanoids leads to slow convergence, often requiring millions of training steps. Reward Design: Sparse rewards make it difficult for the agent to discover meaningful behaviors, while dense rewards demand intricate and often brittle design efforts. By introducing a loosely guided reference—a simple trajectory representing the desired flow of the task—we aim to reduce the exploration space while maintaining the flexibility of RL. This approach bridges the gap between pure RL and demonstration-based methods, enabling the learning of complex maneuvers like climbing, jumping, and dynamic obstacle traversal without heavy reliance on reward engineering or exact demonstrations.
Keywords
humanoid, reinforcement learning, loosely guided
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Master Thesis
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Published since: 2025-02-25
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Information, Computing and Communication Sciences
Benefits and challenges of promoting minimally supervised therapy in a rehabilitation clinic
Increasing the therapy time can benefit patients in multiple ways. Group therapy (e.g., technology-assisted) allows clinics to increase the therapy dose for patients without increasing the workload for therapists. However, in practical implementation, some challenges often arise (e.g., patients not liking it) that limit the efficacy of group therapy. The aim of this project is to gain practical experience with group therapy sessions (i.e., supporting group therapy sessions at the Clinica Hildebrand in Brissago), identify barriers and benefits related to group therapy, and propose guidelines to improve the integration of group therapy in clinical practice.
Keywords
practical internship, research internship, clinical work, technology-assisted therapy, group therapy, minimally supervised therapy
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Internship
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Published since: 2025-02-12 , Earliest start: 2025-03-01 , Latest end: 2025-10-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Università della Svizzera italiana
Organization Rehabilitation Engineering Lab
Hosts Devittori Giada
Topics Medical and Health Sciences
Supervised learning for loco-manipulation
To spot arm operations, we propose a multi-phase approach combining supervised learning and reinforcement learning (RL). First, we will employ supervised learning to develop a model for solving inverse kinematics (IK), enabling precise joint angle calculations from desired end-effector pose. Next, we will utilize another supervised learning technique to build a collision avoidance model, trained to predict and avoid self-collisions based on arm configurations and environmental data. With these pre-trained networks, we will then integrate RL to generate dynamic and safe arm-motion plans. The RL agent will leverage the IK and collision avoidance models to optimize arm trajectories, ensuring efficient and collision-free movements. This entire pipeline could be back propagated while promising to enhance the accuracy, safety, and flexibility of robotic arm operations in complex environments.
Keywords
Spot, Supervised learning, loco-manipulation
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-10 , Earliest start: 2025-02-10 , Latest end: 2026-03-01
Organization Robotic Systems Lab
Hosts Mirrazavi Sina
Topics Information, Computing and Communication Sciences
Model-Based Reinforcement Learning for Loco-manipulation
This project aims to develop a model-based reinforcement learning (RL) framework to enable quadruped robots to perform dynamic locomotion and manipulation simultaneously by leveraging advanced model-based RL algorithms such as DeamerV3, TDMPC2 and SAM-RL. We will develop control policies that can predict future states and rewards, enabling the robot to adapt its behavior on-the-fly. The primary focus will be on achieving stable and adaptive walking patterns while reaching and grasping objects. The outcome will provide insights into the integration of complex behaviors in robotic systems, with potential applications in service robotics and automated object handling.
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-10 , Earliest start: 2025-02-10 , Latest end: 2026-02-10
Organization Robotic Systems Lab
Hosts Mirrazavi Sina
Topics Information, Computing and Communication Sciences
Integrating OpenVLA for Vision-Language-Driven Loco-Manipulation robotics scenarios
This thesis proposes to integrate and adapt the OpenVLA (Open-Source Vision-Language-Action) model to control the Spot robotic arm for performing complex grasping and placing tasks. The study will focus on enabling the robot to recognize, grasp, and organize various toy-sized kitchen items based on human instructions. By leveraging OpenVLA's robust multimodal capabilities, this project aims to bridge the gap between human intent and robotic actions, enabling seamless task execution in unstructured environments. The research will explore the feasibility of fine-tuning OpenVLA for task-specific operations and evaluate its performance in real-world scenarios, providing valuable insights for advancing multimodal robotics.
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-10 , Earliest start: 2025-02-10 , Latest end: 2026-02-10
Organization Robotic Systems Lab
Hosts Mirrazavi Sina
Topics Information, Computing and Communication Sciences
Research Assistant in Biosensing for Robotics Care and Body Simulation (~12 months)
Join a team of scientists improving the long-term prognosis and treatment of Spinal Cord Injury (SCI) through mobile and wearable systems and personalized health monitoring. Joining the SCAI Lab part of the Sensory-Motor Systems Lab at ETH, you will have the unique opportunity of working at one of the largest and most prestigious health providers in Switzerland: Swiss Paraplegic Center (SPZ) in Nottwil (LU).
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App development, Machine Learning, Data bases, Data engineering, Systems Engineering, Data Modelling
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Internship , Lab Practice , Student Assistant / HiWi , ETH Zurich (ETHZ)
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Published since: 2025-02-06 , Earliest start: 2025-03-03 , Latest end: 2026-12-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , IBM Research Zurich Lab , Institute for Research in Biomedicine , Hochschulmedizin Zürich , Swiss Institute of Bioinformatics , University of Lucerne , University of Zurich , Zurich University of Applied Sciences , Zurich University of the Arts , Lucerne University of Applied Sciences and Arts , Berner Fachhochschule
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.
Topics Information, Computing and Communication Sciences
Master's Thesis: AI-powered nap detection from Fitbit data
The uprise of consumer-grade fitness trackers has opened the doors to long-term activity monitoring in the wild in research and clinics. However, Fitbit does not identify napping episodes shorter than 90 minutes. Hence, there is a need to establish a robust algorithm to detect naps.
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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
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.
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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.
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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.
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LIDAR, 3D reconstruction, Isaac gym, deep learning, perception, reinforcement learning
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Semester Project , Master Thesis
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Published since: 2025-02-03 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Höller David , Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
Depth Estimation with Event-Based Cameras on nano-UAVs
develop a neural network for depth estimation on nano-drones, using miniaturized event-based cameras, for autonomous navigation
Keywords
nano-drones, robotics, autonomous navigation, depth-estimation, event-based cameras, neuromorphic, neuromorphic vision, embedded systems, tinyML, artificial intelligence, deep learning.
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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-03 , Earliest start: 2025-02-03 , Latest end: 2025-12-31
Organization Digital Circuits and Systems (Benini)
Hosts Lamberti Lorenzo
Topics Engineering and Technology
Digital HW design: accelerator for Event-Based Convolutional Neural Network
Digital HW design: accelerator for Event-Based Convolutional Neural Network
Keywords
Digital HW design, PULP, RISC-V
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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-03 , Earliest start: 2025-02-03 , Latest end: 2025-07-31
Organization Digital Circuits and Systems (Benini)
Hosts Lamberti Lorenzo
Topics Engineering and Technology
Autonomous nano-drone racing CNN on next-gen ULP SoC
Develop a visual-based neural network for autonomous nano-drone racing
Keywords
nano-drones, robotics, autonomous navigation, embedded systems,artificial intellicence, deep learning, microcontroller
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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-02-03 , Latest end: 2025-07-31
Organization Digital Circuits and Systems (Benini)
Hosts Lamberti Lorenzo
Topics Engineering and Technology
Reiforcement Learning of Pretrained Trasformer Models
We want to train RL agents on our new particle simulator, accelerated on the GPU via warp in Isaac sim.
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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
Hand-object 3D reconstruction from Internet videos (Computer Vision)
The goal of the project is to reconstruct 3D hands and objects from internet videos.
Keywords
Computer vision, VR/AR, 3D reconstruction, 3D pose estimation, machine learning, neural networks, human-object interactions
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Semester Project , Master Thesis
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Published since: 2025-02-01 , Earliest start: 2024-11-18 , Latest end: 2025-08-20
Applications limited to ETH Zurich , Department of Computer Science , Eberhard Karls Universität Tübingen
Organization Advanced Interactive Technologies
Hosts Fan Zicong
Topics Information, Computing and Communication Sciences
A Bayesian sensor fusion and machine learning approach for robust hand gesture decoding with application to stroke rehabilitation.
About the project: This thesis aims to design a framework for robust fine-motor action decoding using multi-modal (sEMG and depth sensing camera) Bayesian sensor fusion and machine learning approach
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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
Diffusion-based Shared Autonomy System for Telemanipulation
Robots may not be able to complete tasks fully autonomously in unstructured or unseen environments, however direct teleoperation from human operators may also be challenging due to the difficulty of providing full situational awareness to the operator as well as degradation in communication leading to the loss of control authority. This motivates the use of shared autonomy for assisting the operator thereby enhancing the performance during the task. In this project, we aim to develop a shared autonomy framework for teleoperation of manipulator arms, to assist non-expert users or in the presence of degraded communication. Imitation learning, such as diffusion models, have emerged as a popular and scalable approach for learning manipulation tasks [1, 2]. Additionally, recent works have combined this with partial diffusion to enable shared autonomy [3]. However, the tasks were restricted to simple 2D domains. In this project, we wish to extend previous work in the lab using diffusion-based imitation learning, to enable shared autonomy for non-expert users to complete unseen tasks or in degraded communication environments.
Keywords
Imitation learning, Robotics, Manipulation, Teleoperation
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Semester Project , ETH Zurich (ETHZ)
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Published since: 2024-12-02 , Earliest start: 2024-11-01 , Latest end: 2025-11-01
Applications limited to ETH Zurich , University of Zurich
Organization Robotic Systems Lab
Hosts Elanjimattathil Aravind
Topics Information, Computing and Communication Sciences , Engineering and Technology