Projects and Theses
Perceptive Arm Motion Planning and Control for Heavy Construction Machine Tasks
In this work we would utilize reinforcement learning, neural network actuator modeling, and perception for the control and arm motion planning of a 40ton excavator with a free-swinging gripper. The project will be in collaboration with Gravis Robotics, ETH spinoff working on the automation of heavy machinery.
Keywords
reinforcement learning, perception, hydraulics, excavator, manipulation, industry
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Semester Project , Collaboration , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-01-08 , Earliest start: 2025-02-03 , Latest end: 2025-08-31
Organization Robotic Systems Lab
Hosts Egli Pascal Arturo , Nan Fang , Spinelli Filippo
Topics Information, Computing and Communication Sciences , Engineering and Technology
Boosting Reinforcement Learning with High-Speed Gaussian Splatting
This project addresses the computational bottlenecks in model-free reinforcement learning (RL) with high-dimensional image inputs by optimizing Gaussian Splatting—a GPU-accelerated technique for photorealistic image generation from point clouds—for RL applications. By integrating pre-sorting methods, the project aims to enhance rendering speeds, enabling broader RL applications beyond geometric constraints or abstraction layers. Building on previous work involving risk annotations in Gaussian splats, the project seeks to develop generalizable RL policies that leverage real-world knowledge.
Keywords
Gaussian Splatting, Navigation, Reinforcement Learning
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Semester Project , Master Thesis
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Published since: 2025-01-08
Organization Robotic Systems Lab
Hosts Roth Pascal , Wilder-Smith Max
Topics Information, Computing and Communication Sciences
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: 2025-01-08 , Earliest start: 2024-12-15 , Latest end: 2025-06-01
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Organization Robotic Systems Lab
Hosts Bhardwaj Arjun , Zurbrügg René
Topics Information, Computing and Communication Sciences
Optimization and testing on healthy participants of a smart sock with textile pressure sensors
The goal of the project is to test and optimize a smart sock prototype for plantar pressure measurements that was previously developed in the lab. The prototype will be optimized based on its ability to track pressure during everyday activities as a wearable device. Tests on healthy participants performing standard movements (e.g., walking, climbing stairs, etc) will be performed to compare the sock performance to a commercial gold standard smart insole system. This technology can be used for plantar pressure monitoring in diverse wearable applications ranging from healthcare to sports.
Keywords
wearable technology, smart sock, plantar pressure, e-textiles, gait analysis
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Semester Project , Master Thesis
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Published since: 2025-01-07 , Earliest start: 2025-01-06 , Latest end: 2025-10-01
Organization Biomedical and Mobile Health Technology Lab
Hosts Galli Valeria
Topics Engineering and Technology
Masters Thesis: Force Sensing with Series Elastic Actuation Component for Surgical Robot
The "in SEA2 SpineBot" Project aims to develop a robotic impedance measurement device capable of assessing the biomechanical properties of the adolescent spine with patient-specific anatomy. This highly interdisciplinary project will be the first of its kind to acquire in vivo data from patients with idiopathic adolescent scoliosis (AIS) during their correction surgery. It is a collaboration with the Children's Hospital of Basel (UKBB) and the Computational Bioengineering Group at ARTOG University of Bern. Ensuring the functionality and reliability of the series elastic actuation (SEA) component of this device is a challenge in sensor development and signal electronics and is paramount to the project's overall success.
Keywords
Series Elastic Actuation Force sensing Surgical Robotics
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Master Thesis
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Published since: 2025-01-06 , Earliest start: 2025-02-01 , Latest end: 2025-04-01
Organization Bio-Inspired RObots for MEDicine-Laboratory (BIROMED-Lab)
Hosts Gerig Nicolas, Dr.
Topics Engineering and Technology
Master Thesis: Sterile Locking Interface and Surgical Workflow Development for Surgical Robot
The "in SEA2 SpineBot" Project aims to develop a robotic impedance measurement device capable of assessing the biomechanical properties of the adolescent spine with patient-specific anatomy. This highly interdisciplinary project will be the first of its kind to acquire in vivo data from patients with idiopathic adolescent scoliosis (AIS) during their correction surgery. It is a collaboration with the Children's Hospital of Basel (UKBB) and the Computational Bioengineering Group at ARTOG University of Bern. A 6 DoF parallel robot has been designed for this purpose and attaches to the vertebrae intraoperatively through pre-installed pedicle screws.
Keywords
Surgical Robot User Inteface Surgical Engineering
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Master Thesis
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Published since: 2025-01-06 , Earliest start: 2025-02-01 , Latest end: 2025-04-01
Organization Bio-Inspired RObots for MEDicine-Laboratory (BIROMED-Lab)
Hosts Gerig Nicolas, Dr. , Sommerhalder Michael
Topics Engineering and Technology
Lumios: Engineering high-throughput muscle tissues for drug development research
In the tissue engineering & biofabrication lab, we have developed a new bioprinting technology that enables the production of highly anisotropic, microstructured hydrogels and facilitates the cultivation of aligned tissues such as skeletal muscle or nerves. On this basis, we are currently working towards establishing the ETH Spin-off Lumios. In a previous proof-of-concept study, we were able to show that embedding myoblasts into these scaffolds, 14 days later, led to the formation of functional mini-muscles that showed similar contractile and biochemical properties as we see in native muscle tissues. Based on these promising results, we now want to integrate these tissues into a platform that enables their culture and characterization in a multi-well plate format and makes them accessible to drug development research for muscle-related diseases like myocardial infarction necrosis, sarcopenia or Duchenne muscular dystrophy.
Keywords
Bioprinting, Tissue engineering, Materials science, Cell culture, Entrepreneurship
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Semester Project , Master Thesis
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Published since: 2025-01-06
Organization Zenobi-Wong Group / Tissue Engineering and Biofabrication
Hosts Weber Patrick
Topics Engineering and Technology
A generalized sEMG-based gesture recognition framework using deep learning approach
This thesis aims to develop a generalizable (user-invariant and session-invariant) gesture recognition framework using deep neural networks
Keywords
sEMG, deep learning, manifold, gesture decoding
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Master Thesis
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Published since: 2025-01-05 , Earliest start: 2025-02-01 , Latest end: 2026-03-01
Organization Sensory-Motor Systems Lab
Hosts Dash Adyasha
Topics Information, Computing and Communication Sciences , Engineering and Technology
VR-based Motor-cognitive rehabilitation with multi-modal sensing
This thesis aims to design a pool of virtual-reality based rehabilitation tasks for simultaneous motor-cognitive rehabilitation for stroke patients
Keywords
virtual reality, sEMG, depth sensing camera, rehabilitation,
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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-01-02 , Earliest start: 2025-03-01 , Latest end: 2025-12-31
Organization Sensory-Motor Systems Lab
Hosts Dash Adyasha
Topics Engineering and Technology
Securing the Future: Enhance Human Safety in Robot Decision Making
Robots have become increasingly advanced recently, capable of performing challenging tasks such as taking elevators and cooking shrimp. Moreover, their ability to accomplish long-horizon tasks given simple natural language instructions is also made possible by large language models. However, with this increased functionality comes the risk that intelligent robots might unintentionally or intentionally harm people based on instructions from an operator. On the other hand, significant efforts have been made to restrain large language models from generating harmful content. Can these efforts be applied to robotics to ensure safe interactions between robots and humans, even as robots become more capable? This project aims to answer this question. This is a collaborative work between ETH, NUS, UCL, Stanford, and Northwestern University. More information will be available during the interview.
Keywords
human safety, large language models, human-robot interaction
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Semester Project , Master Thesis
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Published since: 2025-01-02
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Robotic Systems Lab
Hosts Shi Fan , Qu Kaixian
Topics Information, Computing and Communication Sciences , Engineering and Technology
Unraveling Calcium Dynamics and Immune Interactions in Bone Graft Substitute Environments through Advanced Ratiometric Imaging
This project endeavors to explore the dynamic interplay among calcium ions, bone graft substitutes, and resident immune cells in both orthotopic and ectopic environments, employing advanced ratiometric imaging techniques.
Keywords
Bone Graft Substitute, Calcium, Ratiometric Imaging, Immune Cells, in vitro, in vivo, Intravital Microscopy
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-12-24 , Earliest start: 2024-10-01 , Latest end: 2025-06-30
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Wissmann Stefanie
Topics Engineering and Technology , Biology
Language-Conditioned Interaction Trajectories for Robotic Manipulation
The project aims to explore how language conditioning can be integrated with vision encoders to accurately predict interaction trajectories for robotic manipulation tasks.
Keywords
egocentric vision, diffusion models, representation learning, multimodal models, robotic manipulation
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Master Thesis
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Published since: 2024-12-23 , Earliest start: 2024-12-23 , Latest end: 2025-08-01
Organization Computer Vision and Geometry Group
Hosts Zurbrügg René , Gavryushin Alexey , Portela Tifanny
Topics Information, Computing and Communication Sciences
Maximum flexion: a problem for markerless tracking?
This internship/research project/semester thesis elaborates why in various of our data collections markerless tracking in maximum flexion became very inaccurate.
Keywords
kinematic analysis, pose detection, robustness
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Semester Project , Internship , Bachelor Thesis
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Published since: 2024-12-22 , Earliest start: 2025-02-18 , Latest end: 2025-10-31
Applications limited to ETH Zurich
Organization Sensory-Motor Systems Lab
Hosts Wolf Peter
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Maximal finger strength: How to assess it?
Various methods have been applied, each probably influenced by different constraints. This internship/master thesis aims to establish a robust standard method.
Keywords
climbing, assessment
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Internship , Master Thesis
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Published since: 2024-12-22 , Earliest start: 2025-01-15 , Latest end: 2025-09-15
Applications limited to ETH Zurich
Organization Sensory-Motor Systems Lab
Hosts Wolf Peter
Topics Medical and Health Sciences , Engineering and Technology
Developing a real-time state-machine for closed-loop brain-machine interfaces
Developing a state-machine Simulink model to be deployed at MathWorks SpeedGoat real-time target machine for closed-loop brain-machine interface (BMI). The state-machine will control the closed-loop BMI peripherals and synchronise the data flow. Peripherals include neural recorders & stimulators, data analysis cluster, video cameras and experimental chamber. Experimental chamber (variety of servos, steppers, sensors etc.) will be controlled with built-in FPGA and GPIO of SpeedGoat machine. Other peripherals are connected with serial bus. Acquired data needs to be organized and stored in datasink unit. Skills: Matlab Simulink, state-machines, FPGA programming, serial communication protocols, data synchronisation Please send your CV and transcript along with your application.
Keywords
Brain-machine interfaces, neural recording, neural stimulation, state-machines, real-time systems, serial communication, FPGA
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-12-20
Organization Neurotechnology
Hosts Özil Eminhan
Topics Engineering and Technology
Development of a Heterocellular Human Bone Organoid for Precision Medicine and Treatment
Our goal is to establish a heterocellular 3D printed bone organoid model comprising all major bone cell types (osteoblasts, osteocytes, osteoclasts) to recapitulate bone remodeling units in an in vitro system. The organoids will be produced with the human cells, as they could represent human pathophysiology better than animal models, and eventually could replace them. These in vitro models could be used in the advancement of next-generation personalised treatment strategies. Our tools are different kinds of 3D bioprinting platforms, bio-ink formulations, hydrogels, mol-bioassays, and time-lapsed image processing of micro-CT scans.
Keywords
3D printing, bone organoids, co-culture, bioreactor, hydrogels, drug testing
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Semester Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-12-19 , Earliest start: 2022-08-01 , Latest end: 2025-11-30
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Steffi Chris
Topics Engineering and Technology , Biology
Developing a GUI for cross-modality brain-machine interfaces
Programming a graphical user interface (e.g. in Qt/C++) which can handle and process the data acquired in our brain-machine interface (BMI) experiments. The data includes high-density brain activity recordings from hundreds of recording channels, neural-stimulation events, 3D&4D data coming from MRI scans of the subject implanted with BMI. The backend will be programmed in Python where you also need to connect supporting tools (e.g. Blender) via Python. Please send an email with your CV and transcript of records attached.
Keywords
Brain-machine interface, BMI, graphical user interface, GUI, Qt, C, C++, Python, Blender, MRI, electrophysiology
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Semester Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-12-19
Organization Neurotechnology
Hosts Özil Eminhan
Topics Information, Computing and Communication Sciences , Engineering and Technology
Imitation Learning for Pedipulation
This project aims to enable our quadrupedal robot, ANYmal, to perform manipulation tasks with its foot. Particularly, we want to use imitation learning to learn high-level motion plans that can solve real-world tasks such as opening doors, pushing objects, and transporting payloads. This approach will allow solving simple manipulation tasks autonomously.
Keywords
legged robots, imitation learning, manipulation, pedipulation
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Semester Project , Master Thesis
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Published since: 2024-12-19 , Earliest start: 2025-02-01 , Latest end: 2025-08-31
Organization Robotic Systems Lab
Hosts Arm Philip
Topics Information, Computing and Communication Sciences , Engineering and Technology
Navigation in 3D Environments with a Climbing Robot
In this project, you will integrate a path-planning algorithm for the magnetic climbing robot magnecko. Your work will enable the robot to navigate on 3-dimensional structures like beams, walls, and ceilings.
Keywords
Climbing robot, legged robot, navigation, path-planning
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Semester Project , Bachelor Thesis
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Published since: 2024-12-19 , Earliest start: 2025-02-01 , Latest end: 2025-08-31
Organization Robotic Systems Lab
Hosts Arm Philip , Roth Pascal
Topics Information, Computing and Communication Sciences , Engineering and Technology
Development of a (gamified) finger training software for a clinical setting
This project focuses on developing an innovative software solution to enhance finger individuation training in neurorehabilitation. Building on an existing device that measures precise finger forces and is currently used for assessments, the goal is to extend its capabilities into an interactive training platform. The project involves designing and implementing the software, transitioning to modern frameworks like Unity or Python for improved performance and usability. With the freedom to create engaging, gamified training experiences and intuitive user interfaces, this project provides a unique opportunity to apply software development skills to a meaningful, real-world application.
Keywords
software development, gamified therapy, neurorehabilitation training
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-12-19 , Earliest start: 2025-01-13 , Latest end: 2025-09-30
Organization Rehabilitation Engineering Lab
Hosts Knill Anna
Topics Information, Computing and Communication Sciences , Engineering and Technology
RL-based Hybrid Control for Manipulation
Legged mobile manipulation is advancing rapidly, with applications in home assistance and robotic maintenance. While learning-based methods excel at end-effector position tracking, many real-world tasks require force control. This project develops a hybrid control policy for legged manipulators to apply force along a principal axis while controlling position tangentially. An example task is whipping a board, where the robot maintains consistent perpendicular force while moving the end-effector on the plane.
Keywords
reinforcement learning, legged robotics, hybrid control
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2024-12-19 , Earliest start: 2025-01-05
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Portela Tifanny , Arm Philip
Topics Information, Computing and Communication Sciences , Engineering and Technology
Master Thesis Project – Optical-based flow scanning and calibration for in situ printing
Join us for an exciting project towards the next level in intraoperative robotic adaptability!
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Published since: 2024-12-18 , Earliest start: 2025-01-01 , Latest end: 2025-12-31
Organization Sensory-Motor Systems Lab
Hosts Sommerhalder Michael
Topics Medical and Health Sciences , 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: 2024-12-18 , 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: 2024-12-18 , 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
Global robot localization in mesh models
This project includes designing and implementing a particle filter for global robot localization in the environments represented by a mesh model. Particular interest lies in the applicability of such a technique for construction sites, which are considered highly dynamic and, hence, challenging for robot localization.
Keywords
* Global robot localization * Diffusion models * Particle Filter
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Semester Project , Master Thesis
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Published since: 2024-12-17 , Earliest start: 2024-12-18
Organization Robotic Systems Lab
Hosts Vysotska Olga , Talbot William
Topics Engineering and Technology
Graph-based robot localization in BIM models
This project focuses on implementing and deploying a graph-based localization framework that allows to position a robot within BIM (Building information Models).
Keywords
Graph-based robot localization BIM models
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Semester Project , Bachelor Thesis
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Published since: 2024-12-17 , Earliest start: 2024-12-18
Organization Robotic Systems Lab
Hosts Vysotska Olga , Talbot William
Topics Engineering and Technology
Digital Twin for Spot's Home
MOTIVATION ⇾ Creating a digital twin of the robot's environment is crucial for several reasons: 1. Simulate Different Robots: Test various robots in a virtual environment, saving time and resources. 2. Accurate Evaluation: Precisely assess robot interactions and performance. 3. Enhanced Flexibility: Easily modify scenarios to develop robust systems. 4. Cost Efficiency: Reduce costs by identifying issues in virtual simulations. 5. Scalability: Replicate multiple environments for comprehensive testing. PROPOSAL We propose to create a digital twin of our Semantic environment, designed in your preferred graphics Platform to be able to simulate Reinforcement Learning agents in the digital environment, to create a unified evaluation platform for robotic tasks.
Keywords
Digital Twin, Robotics
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Semester Project , Master Thesis
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Published since: 2024-12-17 , Earliest start: 2025-01-05
Applications limited to University of Zurich , ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Computer Vision and Geometry Group
Hosts Blum Hermann , Portela Tifanny , Bauer Zuria, Dr. , Trisovic Jelena
Topics Information, Computing and Communication Sciences
KALLAX Benchmark: Evaluating Household Tasks
Motivation ⇾ There are three ways to evaluate robots for pick-and-place tasks at home: 1. Simulation setups: High reproducibility but hard to simulate real-world complexities and perception noise. 2. Competitions: Good for comparing overall systems but require significant effort and can't be done frequently. 3. Custom lab setups: Common but lead to overfitting and lack comparability between labs. Proposal ⇾ We propose using IKEA furniture to create standardized, randomized setups that researchers can easily replicate. E.g, a 4x4 KALLAX unit with varying door knobs and drawer positions, generating tasks like "move the cup from the upper right shelf into the black drawer." This prevents overfitting and allows for consistent evaluation across different labs.
Keywords
Benchmakr, Robotics, pick-and-place
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Semester Project , Master Thesis
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Published since: 2024-12-17 , Earliest start: 2025-01-06
Applications limited to University of Zurich , ETH Zurich , Swiss National Science Foundation , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Computer Vision and Geometry Group
Hosts Blum Hermann , Bauer Zuria, Dr. , 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: 2024-12-16 , Earliest start: 2023-09-15 , Latest end: 2024-05-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Ahmadizadeh Chakaveh
Topics Information, Computing and Communication Sciences , Engineering and Technology
Develop software for wearable technologies
The aim of this project is to develop mobile software to communicate with our already developed textile-based wearable technology and process sensor data for movement monitoring.
Keywords
smart clothing, wearable technology, software development, fitness tracking, sports medicine, mobile application, computer science
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Semester Project , Bachelor Thesis
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Published since: 2024-12-16 , Earliest start: 2024-07-22 , Latest end: 2025-08-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Ahmadizadeh Chakaveh
Topics Information, Computing and Communication Sciences , Engineering and Technology
Language-Conditioned Interaction Trajectories Using Diffusion Algorithms for Robotic Manipulation
Train a diffusion model to predict 3D interaction trajectories from ego-centric images and task descriptions. The final model will be evaluated on a real robotic arm with five fingered hand.
Keywords
egocentric vision, diffusion models, representation learning, multimodal models, robotic manipulation
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-12-16
Organization Robotic Systems Lab
Hosts Zurbrügg René , Zurbrügg René
Topics Information, Computing and Communication Sciences
Flexible Wireless Sensing Node for Continuous Body Monitoring
Advancements in sensor technology, low-power mixed-signal/RF circuits, and Wireless Sensor Networks (WSNs) have enabled the creation of compact, cost-effective solutions for healthcare applications. A notable development in this field is the Body Sensor Network, which is designed to monitor the human body for healthcare purposes.
Keywords
Embedded systems, PCB design, Firmware development, Data Analysis
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Semester Project , Internship , Bachelor Thesis
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Published since: 2024-12-14 , Earliest start: 2025-01-12
Organization Digital Circuits and Systems (Benini)
Hosts Spacone Giusy
Topics Engineering and Technology
Continual Learning for Adaptive Hand Gesture Recognition with A-mode Ultrasound
Hand Gesture Recognition has gained significant attention in recent years due to its potential applications in various fields, including interaction with virtual environments (like the Metaverse), teleoperation, and prosthetic device control. Multiple sensing techniques can be employed for hand movement recognition, including vision-based sensors (cameras), mechanical sensors (e.g., IMUs), sEMG, and the more recent and increasingly popular Ultrasound (US). US enables high-spatial (submillimeter) and temporal (fraction of a millisecond) resolution imaging of deep musculoskeletal structures. While several studies [1], [2], [3] have used US for hand gesture recognition, challenges remain in ensuring robustness against factors like sensor shift, donning and doffing, varying muscle force, and interday use [4].
Keywords
Ultrasound, Hand Gesture Recognition, TinyML, Continual Learning
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2024-12-14 , Earliest start: 2024-12-10
Organization Digital Circuits and Systems (Benini)
Hosts Spacone Giusy
Topics Information, Computing and Communication Sciences , Engineering and Technology
Clinical investigation of the pathophysiology and reciprocal relationships between progressive disc degeneration and spinal (mal-) alignment
Lumbar intervertebral disc degeneration (LDD) is estimated to affect 400M individuals worldwide annually, which causes pain and disability for the patients affected by it [1]. Continuous localized, and structural degeneration on multiple intervertebral disc (IVD) levels can progress to accumulated macroscopic deformities, manifesting in abnormal spinal curvatures, also known as Adult Spinal Deformities (ASD). LDD and spinal alignment are hypothesized to be closely interrelated, with specific alignment parameters showing mutual correlations with the degree of LDD [2]. Through analyzing longitudinal data - including X-ray-derived biplanar alignment parameters and MRI-based Pfirrmann grading - this study aims to identify extensive patterns between alignment characteristics and the individual degenerative state of lumbar IVDs. Such insights could improve our understanding of degenerative cascades and influence decision making in clinical treatment approaches by identifying alignment profiles at higher risk for degeneration. The proposed study includes a clinical cohort of degenerative lumbar back pain patients who were treated conservatively, and followed up over a period of 3-5 years, before eventually undergoing surgery at Schulthess Clinic Zurich. This project is jointly supervised by an interdisciplinary group of researchers and clinicians of ETH Zurich and Schulthess clinic Zurich, which offers insights into fundamental spinal research, as well as daily clinical practice. Your tasks will include data annotation, model development, and collaborative discussions of results, providing a comprehensive experience in interdisciplinary research. This project offers an excellent opportunity to engage in impactful research at the intersection of biomechanics, clinical practice, and data-driven modeling.
Keywords
Spinal Alignment, Intervertebral Disc Degeneration, Adult Spinal Deformity, Clinical Research, Longitudinal Study
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Semester Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-12-13 , Earliest start: 2025-02-01
Organization Musculoskeletal Biomechanics
Hosts Rieger Florian
Topics Medical and Health Sciences , Engineering and Technology
Safe RL for Robot Social Navigation
Developing a constrained RL framework for social navigation, emphasizing explicit safety constraints to reduce reliance on reward tuning.
Keywords
Navigation, Robot Planning, Reinforcement Learning, RL, Social Navigation
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Master Thesis
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Published since: 2024-12-13 , Earliest start: 2025-01-01 , Latest end: 2025-12-31
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Alyassi Rashid , Alyassi Rashid , Alyassi Rashid
Topics Engineering and Technology
Bridging RL-based Robot Navigation & Crowd Simulation
Designing a crowd simulator for realistic human-robot interactions, enabling RL agent training in social navigation tasks.
Keywords
Navigation, Robot Planning, Reinforcement Learning, RL, Social Navigation.
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Master Thesis
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Published since: 2024-12-12 , Earliest start: 2025-01-01 , Latest end: 2025-12-01
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Alyassi Rashid , Alyassi Rashid , Alyassi Rashid
Topics Engineering and Technology
MICRO-MULTI-PHYSICS AGENT-BASED MODELLING OF THE TRABECULAR BONE RESPONSE TO ESTROGEN DEPLETION
The proposed project will investigate the trabecular bone response to estrogen depletion and will be used to investigate the probability of the chosen mechanism of action for estrogen. The chosen mechanism of action will be validated using available experimental reference data.
Keywords
Osteoporosis, trabecular bone, python programming, simulation
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-12-12 , Earliest start: 2024-09-01 , Latest end: 2025-08-31
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Schulte Friederike
Topics Medical and Health Sciences , Information, Computing and Communication Sciences
Adapting to Injuries for Dexterous In-Hand Manipulation
Develop a reinforcement learning-based method for training adaptive policies for dexterous in-hand manipulation systems to deal with actuator failure on the fly.
Keywords
Dexterous Manipulation, Reinforcement Learning, Adaptive Learning
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Semester Project , Master Thesis
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Published since: 2024-12-12 , Earliest start: 2024-12-16 , Latest end: 2025-06-01
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Robotic Systems Lab
Hosts Bhardwaj Arjun , Ma Yuntao
Topics Information, Computing and Communication Sciences
Extending Functional Scene Graphs to Include Articulated Object States
While traditional [1] and functional [2] scene graphs are capable of capturing the spatial relationships and functional interactions between objects and spaces, they encode each object as static, with fixed geometry. In this project, we aim to enable the estimation of the state of articulated objects and include it in the functional scene graph.
Keywords
scene understanding, scene graph, exploration
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Semester Project , Master Thesis
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Published since: 2024-12-11
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Computer Vision and Geometry Group
Hosts Bauer Zuria, Dr. , Trisovic Jelena , Zurbrügg René
Topics Information, Computing and Communication Sciences , Engineering and Technology
Offline LLM-Based Planning for Mobile Manipulation
Large Language Models have enormous potential to increase the generalization capability of robots. Specifically, they can allow mobile platforms to navigate successfully in unknown environments towards open-vocabulary goals. Current applications to mobile platforms are limited due to online models being both slow to query and requiring stable internet connection, which can not always be guaranteed. This project aims to explore the possibility of using a miniature LLM locally on the ALMA robot for navigation though unmapped space, with the goal of moving towards specifying objects using an open-vocabulary and interacting with them.
Keywords
Large Language Models, Mobile Manipulation, Robotics, Machine Learning, Planning
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Semester Project , Master Thesis
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Published since: 2024-12-11 , Earliest start: 2025-02-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Elanjimattathil Aravind , Scheidemann Carmen
Topics Engineering and Technology
Temporal Graphical Modeling for Understanding and Preventing Autonomic Dysreflexia
This project will be based on the preliminary results obtained from a previous master project in causal graphical modeling of autonomous dysreflexia (AD). The focus of the extension would be two-fold. One is improving the temporal graphical reconstruction for understanding the mechanism of AD. The other one is building a forecasting framework for the early detection and prevention of AD using the graph structure we constructed before. Please refer to the attached document for more details about the task description. Based on the candidate's qualifications, funding/allowance can be provided.
Keywords
Graphical Modeling; Graph Neural Networks; Multivariate Time Series; Spinal Cord Injuries; Autonomic Dysreflexia; Wearable Sensing
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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-12-11 , Earliest start: 2025-05-01 , Latest end: 2025-10-31
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr. , Li Yanke , Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences
Active Object Localization with Touch
Develop active exploration strategies for object identification and localization with tactile feedback.
Keywords
Dexterous Manipulation, Object Retrieval, Active Localization, Tactile Sensing
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Semester Project , Master Thesis
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Published since: 2024-12-11 , Earliest start: 2024-12-15 , Latest end: 2025-06-01
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Robotic Systems Lab
Hosts Bhardwaj Arjun , Zurbrügg René
Topics Information, Computing and Communication Sciences
Robustness of Human Pose Estimators on Synthetic Datasets
The aim of this project is the creation of a synthetic dataset for human exercise movements and assessing the robustness of computer vision algorithms towards visual inputs.
Keywords
human pose estimation, motion capturing, synthetic datasets
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Internship , Master Thesis
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Published since: 2024-12-10 , Earliest start: 2025-01-01 , Latest end: 2025-12-31
Organization Sensory-Motor Systems Lab
Hosts Rode David
Topics Engineering and Technology
Assessing the Effect of 3D-printed Orthotic Shoes on OA Patients' Knee Kinematics during Walking
We are looking for a self-motivated Master student to work with us on this exciting project (as her/his Master thesis, semester/internship project). Here, we plan to test the effect of our novel orthotic shoes on OA patients' knee kinematics, especially the contact pattern of joint cartilage during level walking, using VICON and dual-plane fluoroscopy system. The ultimate goal is to provide us with fundamental indications on the design of orthotic shoes for knee osteoarthritis individuals.
Keywords
shoe; orthosis; gait; biomechanics; Master thesis; knee; osteoarthritis; rehabilitation; gait analysis; fluoroscopy
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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-12-10 , Earliest start: 2025-02-28 , Latest end: 2025-07-31
Organization Clinical Movement Biomechanics
Hosts Zhang Qiang
Topics Medical and Health Sciences , Engineering and Technology
Learning-based object orientation prediction for handovers
Humans are exceptional at handovers. Besides timing and spatial precision, they also have a high-level understanding of how the other person wants to use the object that is handed over. This information is needed to hand over an object, such that it can be used directly for a specific task. While robots can reason about grasp affordances, the integration of this information with perception and control is missing.
Keywords
Robot-Human Handover, Human-Robot-Interaction, Mobile Manipulation, Robotics
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Semester Project , Master Thesis
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Published since: 2024-12-10 , Earliest start: 2025-02-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Scheidemann Carmen , Tulbure Andreea
Topics Information, Computing and Communication Sciences , Engineering and Technology
Evolve to Grasp: Learning Optimal Finger Configuration for a Dexterous Multifingered Hand
Use evolutionary algorithms with analytical force closure metrics to learn the optimal morphology of a dexterous hand.
Keywords
Evolutionary Algorithm, Machine Learning, Grasping, Robotics
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Semester Project , Master Thesis
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Published since: 2024-12-09 , Earliest start: 2024-12-09 , Latest end: 2025-10-31
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Church Joseph , Zurbrügg René
Topics Information, Computing and Communication Sciences
Learn to Reach: Collision Aware End-Effector Path Planning & Tracking using Reinforcement Learning
Develop a method for collision aware reaching tasks using reinforcement learning and shape encodings of the environment
Keywords
Reinforcement Learning, Robotics, Perception, Machine Learning
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-12-09 , Earliest start: 2024-12-09 , Latest end: 2025-12-31
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Zurbrügg René , Zurbrügg René , Portela Tifanny
Topics Information, Computing and Communication Sciences
Differential Particle Simulation for Robotics
This project focuses on applying differential particle-based simulation to address challenges in simulating real-world robotic tasks involving interactions with fluids, granular materials, and soft objects. Leveraging the differentiability of simulations, the project aims to enhance simulation accuracy with limited real-world data and explore learning robotic control using first-order gradient information.
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Semester Project , Master Thesis
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Published since: 2024-12-09 , Earliest start: 2025-01-01 , Latest end: 2025-12-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Robotic Systems Lab
Hosts Nan Fang , Ma Hao
Topics Engineering and Technology
Conformal Prediction for Distribution Shift Detection in Online Learning
This project investigates the use of conformal prediction for detecting distribution shifts in online learning scenarios, with a focus on robotics applications. Distribution shifts, arising from deviations in task distributions or changes in robot dynamics, pose significant challenges to online learning systems by impacting learning efficiency and model performance. The project aims to develop a robust detection algorithm to address these shifts, classifying task distribution shifts as outliers while dynamically retraining models for characteristic shifts.
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Semester Project , Master Thesis
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Published since: 2024-12-09 , Earliest start: 2025-01-01 , Latest end: 2025-12-31
Organization Robotic Systems Lab
Hosts Ma Hao , Nan Fang
Topics Information, Computing and Communication Sciences , Engineering and Technology
Conductive polymer pattern deposition for smart textile applications
The goal of the project is to develop a simple and versatile method for production of robust conductive patterns on textile via deposition of conductive polymers. This technology will allow further development of wearable electronics for biomedical applications.
Keywords
wearable, smart textile, conducting polymer, polymerization, capacitance, conductivity
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Bachelor Thesis , Master Thesis
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Published since: 2024-12-06 , Earliest start: 2024-08-01 , Latest end: 2025-08-01
Organization Biomedical and Mobile Health Technology Lab
Hosts Shokurov Aleksandr
Topics Medical and Health Sciences , Engineering and Technology , Chemistry
Visual Language Models for Long-Term Planning in Construction
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: 2024-12-06 , Earliest start: 2025-01-24 , Latest end: 2025-10-29
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
Learning Acrobatic Excavator Maneuvers
Gravis Robotics is an ETH spinoff from the Robotic Systems Lab (RSL) working on the automation of heavy machinery (https://gravisrobotics.com/). In this project you will be working with the Gravis team to develop an algorithm that allows a 25-ton excavator to perform an acrobatics maneuver, the jump turn.
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Semester Project , Master Thesis
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Published since: 2024-12-06 , Earliest start: 2025-01-01 , Latest end: 2025-12-01
Organization Robotic Systems Lab
Hosts Egli Pascal Arturo , Zhang Weixuan
Topics Engineering and Technology
RL-Based Autonomous Excavation
Gravis Robotics is an ETH spinoff from the Robotic Systems Lab (RSL) working on the automation of heavy machinery (https://gravisrobotics.com/). In this project, you will be working with the Gravis team to develop a vision-based system to detect anomalies during the autonomous operation of the machine. You will conduct your project at Gravis under joint supervision from RSL.
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Published since: 2024-12-06 , Earliest start: 2025-01-01 , Latest end: 2025-12-01
Organization Robotic Systems Lab
Hosts Zhang Weixuan , Egli Pascal Arturo
Topics Engineering and Technology
Data-Driven Joint Control
Gravis Robotics is an ETH spinoff from the Robotic Systems Lab (RSL) working on the automation of heavy machinery (https://gravisrobotics.com/). In this project, you will be working with the Gravis team to develop a perceptive navigation system for an autonomous CAT323 excavator. You will conduct your project at Gravis with joint supervision with RSL.
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Published since: 2024-12-06 , Earliest start: 2025-01-01 , Latest end: 2026-01-01
Organization Robotic Systems Lab
Hosts Jelavic Edo , Egli Pascal Arturo
Topics Engineering and Technology
Full Coverage Navigation for Construction Sites
Monitoring the development of a construction site is essential to ensure the project stays on schedule, within budget, and meets quality and safety standards. This can be achieved through regular 3D scans of the full construction site. However, the current approach demands substantial human time and effort, limiting efficiency and scalability. To address this, we aim to automate the process using a legged robot, capable of navigating complex terrains and performing scans autonomously.
Keywords
Global Planning, Navigation, Autonomy, Construction Side
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Semester Project , Bachelor Thesis
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Published since: 2024-12-06
Organization Robotic Systems Lab
Hosts Richter Julia
Topics Information, Computing and Communication Sciences
Taming the Invisible: Autonomous Gas Detection with ANYmal
Gas source localisation has many applications, such as responding to chemical accidents, detecting explosives, and identifying methane leaks from landfill sites. These tasks often take place in hazardous environments, posing significant risks to human operators. To mitigate these dangers, robotic systems are increasingly being developed to undertake these challenging missions. However, real-world gas localization presents a significant challenge: gas plumes are highly dynamic, exhibiting both temporal and spatial inconsistencies due to environmental factors such as wind, turbulence, and diffusion. Overcoming these complexities is essential for reliable robotic gas detection and localization.
Keywords
Navigation, Gas Sensing, Neural Network
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Semester Project , Master Thesis
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Published since: 2024-12-06
Organization Robotic Systems Lab
Hosts Richter Julia
Topics Information, Computing and Communication Sciences
Sniffing ANYmal: Detecting Explosives Through Smell
The most effective method for locating explosives is through vapor detection, traditionally performed by trained sniffing dogs. While highly effective, these animals are costly to train, have limited operational endurance, and require the constant presence of a handler. Most critically, the significant risks of detonation endanger both the dogs and their handlers, highlighting the need for autonomous solutions in explosive detection.
Keywords
Navigation, Autonomy, Gas Detection
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Semester Project , Bachelor Thesis
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Published since: 2024-12-06
Organization Robotic Systems Lab
Hosts Richter Julia
Topics Information, Computing and Communication Sciences
RL Digging Policy Interpretability
Gravis Robotics is an ETH spinoff from the Robotic Systems Lab (RSL) working on the automation of heavy machinery (https://gravisrobotics.com/). In this project, you will be working with the Gravis team to evaluate interpretability methods for neural networks and use them to analyze undesirable behavior of RL digging policies deployed on real excavators. You will conduct your project at Gravis under joint supervision from RSL.
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Published since: 2024-12-06 , Earliest start: 2025-01-01 , Latest end: 2025-12-01
Organization Robotic Systems Lab
Hosts Egli Pascal Arturo
Topics 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: 2024-12-06 , Earliest start: 2024-09-01 , Latest end: 2025-09-01
Organization Biomedical and Mobile Health Technology Lab
Hosts Shokurov Aleksandr
Topics Engineering and Technology , Chemistry
Point-of-Care Sensor for Urinary Iodine
The goal of the project is to develop a cheap and disposable sensor capable of determination of iodine levels in human urine for early diagnostic purposes.
Keywords
electrochemistry, iodine, nutrition, health, point of care
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Master Thesis
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Published since: 2024-12-06 , 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
Perceptive Navigation
Gravis Robotics is an ETH spinoff from the Robotic Systems Lab (RSL) working on automation of heavy machinery (https://gravisrobotics.com/). In this project you will be working with the Gravis team to develop a perceptive navigation system for autonomous CAT323 excavator. You will conduct your project at Gravis with joint supervision with RSL.
Keywords
Perceptive Navigation, Autonomous Excavator
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Internship , Master Thesis
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Published since: 2024-12-06 , Earliest start: 2025-01-01 , Latest end: 2026-01-01
Organization Robotic Systems Lab
Hosts Jelavic Edo , Egli Pascal Arturo , Cizek Petr
Topics Engineering and Technology
Conductive thread modification for wearable strain sensors
The goal of the project is to modify commercially available conductive yarns to improve their operational properties for potential employment in novel garment-embedded sensors for human motion detection.
Keywords
wearable, smart textile, conductive, e-textile, sensor
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Published since: 2024-12-06 , Earliest start: 2024-10-01
Organization Biomedical and Mobile Health Technology Lab
Hosts Shokurov Aleksandr
Topics Medical and Health Sciences , Engineering and Technology , Chemistry
RL-Based Stockpile Management for Autonomous Excavators
Gravis Robotics is an ETH spinoff from the Robotic Systems Lab (RSL) working on the automation of heavy machinery (https://gravisrobotics.com/). In this project, you will be working with the Gravis team to develop an RL-based perceptive planning and control system for stockpile management for an autonomous excavator. You will conduct your project at Gravis under joint supervision from RSL.
Keywords
Reinforcement Learning, Autonomous Excavation
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Semester Project , Master Thesis
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Published since: 2024-12-06 , Earliest start: 2025-01-01 , Latest end: 2026-01-01
Organization Robotic Systems Lab
Hosts Egli Pascal Arturo
Topics Engineering and Technology
Volumetric Bucket-Fill Estimation
Gravis Robotics is an ETH spinoff from the Robotic Systems Lab (RSL) working on the automation of heavy machinery (https://gravisrobotics.com/). In this project, you will be working with the Gravis team to develop a perceptive bucket-fill estimation system. You will conduct your project at Gravis under joint supervision from RSL.
Keywords
Autonomous Excavation
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Internship
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Published since: 2024-12-06 , Earliest start: 2025-01-01 , Latest end: 2026-01-01
Organization Robotic Systems Lab
Hosts Egli Pascal Arturo
Topics Engineering and Technology
Vision-Based Anomaly Detection for Autonomous Excavators
Gravis Robotics is an ETH spinoff from the Robotic Systems Lab (RSL) working on the automation of heavy machinery (https://gravisrobotics.com/). In this project, you will be working with the Gravis team to develop a vision-based system to detect anomalies during the autonomous operation of the machine. You will conduct your project at Gravis under joint supervision from RSL.
Keywords
Vision, Autonomous Excavator
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Semester Project , Master Thesis
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Published since: 2024-12-06 , Earliest start: 2025-01-01 , Latest end: 2026-01-01
Organization Robotic Systems Lab
Hosts Zhang Weixuan , Egli Pascal Arturo
Topics Engineering and Technology
From Spatial to Functional: Functional Scene Graph for Enhanced Robotic Decision Making
This project explores the concept of Functional Scene Graphs, an extension of traditional scene graphs that capture not just spatial relationships but also the functional interactions between objects and spaces. For example, a light switch enables illumination of a room, or a key provides access to a locked door. Such relationships, while intuitive for humans, are often overlooked in robotics systems, limiting a robot’s ability to reason about and interact with its environment effectively. The core challenge lies in understanding these functional relationships. While a robot might attempt to explore and infer such connections autonomously, humans could assist by demonstrating interactions, offering a means for robots to learn more efficiently. This project will focus on integrating functional understanding into scene graphs, enabling robots to infer high-level semantic interactions and make better decisions during tasks like navigation and manipulation.
Keywords
scene understanding, scene graph, exploration
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Master Thesis
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Published since: 2024-12-03
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Robotic Systems Lab
Hosts Bauer Zuria, Dr. , Qu Kaixian , Zurbrügg René , Blum Hermann
Topics Information, Computing and Communication Sciences , Engineering and Technology
LLM-Driven Skill Acquisition for the ANYmal robot
The goal of this project is to apply LLMs to teach the ANYmal robot new low-level skills via Reinforcement Learning (RL) that the task planner identifies to be missing.
Keywords
Large Language Models, Reinforcement Learning, Robotics
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Master Thesis
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Published since: 2024-12-03
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Roth Pascal , Portela Tifanny
Topics Information, Computing and Communication Sciences
Understanding the influence of motor, sensory, and cognitive factors on manual dexterity
Manual dexterity, the ability to move our hands skillfully, is essential for daily tasks such as eating, writing, and dressing. Neurological conditions like stroke or brain injury can severely impair dexterity, which depends on sensory, motor, and cognitive factors. While impairments in finger individuation and strength are known, the relationships between these components remain unclear. This project aims to measure dexterity-related factors in patients with neurological conditions and healthy individuals in a neurorehabilitation clinic in Switzerland (cereneo). Using standardized clinical assessments and novel (robotic) technologies, the study will provide detailed insights into the contributions of these factors, supporting the development of innovative rehabilitation strategies.
Keywords
Neurorehabilitation, manual dexterity, assessment, hand & finger, robotics, clinic
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Semester Project , Internship , Master Thesis
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Published since: 2024-12-02 , Earliest start: 2025-02-03 , Latest end: 2025-12-19
Organization Rehabilitation Engineering Lab
Hosts Knill Anna
Topics Medical and Health Sciences , Engineering and Technology , Behavioural and Cognitive Sciences
Organ-on-Chip Automation Hardware Design: Development and Integration of a Miniature Robotic XY Stage for a Miniature Microscope
Organs-on-Chip (OoC) replicate human organs in vitro but often lack physiological perfusion profiles. This project aims to develop a compact robotic XY stage for integration with OoC perfusion systems, enhancing automation and precision. By improving compatibility and mimicking dynamic blood flow, this innovation advances research and pharmaceutical applications.
Keywords
Organ; Chip; Automation; Hardware; Design; Robot
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Master Thesis
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Published since: 2024-12-02 , Earliest start: 2025-01-01 , Latest end: 2025-12-31
Organization Sensory-Motor Systems Lab
Hosts Sommerhalder Michael
Topics 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
Bachelor / Semester Thesis: Why can we have driverless cars but are unable to automate therapy?
Help us in quantifying the complexity of human-robot systems across various domains and identify the technical challenges that still persist until today.
Keywords
Human-robot interaction; intention automation; instruction space; metrics; literature research
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Bachelor Thesis
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Published since: 2024-12-02 , Earliest start: 2025-01-01 , Latest end: 2025-12-31
Applications limited to University of Zurich , ETH Zurich , University of Basel , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Sensory-Motor Systems Lab
Hosts Sommerhalder Michael
Topics Information, Computing and Communication Sciences
High dose high intensity proprioception training for neurological patients using a robotic device
Proprioception, often called the "sixth sense," is vital for coordinating movements and maintaining balance, especially in the hands and fingers. In neurological patients, impaired proprioception in the upper limbs can hinder daily tasks and reduce quality of life. Traditional rehabilitation often lacks the intensity and precision needed for optimal recovery of fine motor skills. This project leverages high-dosage training with the ETH MIKE, a validated one-degree-of-freedom robotic device designed for precise, repetitive movements to enhance proprioception and motor function. By promoting neuroplasticity and functional recovery, this research aims to advance rehabilitation practices. Participants will gain hands-on clinical experience, train with neurological patients, and collaborate with therapists, engineers, and researchers.
Keywords
Proprioception, training, neurology, patients, clinic, rehabilitation, neurorehabilitaiton, hand
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-12-02 , Earliest start: 2025-04-28 , Latest end: 2025-10-31
Organization Rehabilitation Engineering Lab
Hosts Knill Anna
Topics Medical and Health Sciences , Engineering and Technology
Master Thesis / Project - SENSEI: Sensor Teaching in Multi-Activity classification from Video and Wearables for Wheelchair Users
In this project, we focus on continuous and quantitative monitoring of activities of daily living (ADL) in SCI individuals with the goal of identifying cardiovascular events and PI-related risk behaviors. ADLs specific to SCI patients and their lifestyles shall be discussed and narrowed down in the scope of this work, therefore an autonomous camera-based system is proposed to classify ADLs. The Current work builds on a previous project where a SlowFast network [1] was trained to identify SCI-specific classes and we aim to further improve the classification and temporal resolution for transferring to wearables' time-series data.
Keywords
Computer vision, activity classification, video processing, Deep Learning, ADL, soft-labelling, probabilistic networks
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Semester Project , Course Project , Internship , Bachelor Thesis , Master Thesis , ETH for Development (ETH4D) (ETHZ) , ETH Zurich (ETHZ)
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Published since: 2024-11-29 , Earliest start: 2024-01-15 , Latest end: 2024-11-01
Applications limited to EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , Zurich University of the Arts , Wyss Translational Center Zurich , University of Zurich , Zurich University of Applied Sciences , CERN , CSEM - Centre Suisse d'Electronique et Microtechnique , Department of Quantitative Biomedicine , Lucerne University of Applied Sciences and Arts , Institute for Research in Biomedicine , IBM Research Zurich Lab , University of St. Gallen , University of Lucerne , University of Lausanne , University of Geneva , University of Fribourg , University of Berne , University of Basel , Université de Neuchâtel , Swiss Institute of Bioinformatics , Swiss National Science Foundation , Swiss Federal Institute for Forest, Snow and Landscape Research , Institute of Robotics and Intelligent Systems D-MAVT , TU Berlin , TU Darmstadt , TU Dresden , RWTH Aachen University , Technische Universität München , Technische Universität Hamburg , Max Planck Society , University of Oxford , University of Leeds , University of Cambridge , UCL - University College London , National Institute for Medical Research , Imperial College London , Royal College of Art , Empa , Università della Svizzera italiana , Hochschulmedizin Zürich , Hong Kong University of Science and Technology , University of Washington , Tokyo Institute of Technology , The University of Tokyo
Organization Sensory-Motor Systems Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Behavioural and Cognitive Sciences
Internships (Industrial or Research) on Body Modelling and Sensing Technology for Health Care in SCI
This hands-on work (internship or semester project) within a clinical setting will bring you close to intelligent health management while exploring multiple data systems. You will experience multimodal data of robotics rehabilitation, general clinical practice, and detailed clinical studies applied in classification and dimensionality reduction.
Keywords
Machine learning, time-series, HR, ECG, BP, wearables, nearables, Medical and health science, healthcare, Android studio, App development
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Semester Project , Internship , Lab Practice , Bachelor Thesis , Master Thesis , Other specific labels , ETH Zurich (ETHZ)
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Published since: 2024-11-29 , Earliest start: 2025-02-03 , Latest end: 2025-12-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa , Eawag , Zurich University of the Arts , Zurich University of Applied Sciences , Wyss Translational Center Zurich , University of Zurich , University of St. Gallen , University of Lucerne , University of Lausanne , University of Geneva , University of Fribourg , University of Berne , University of Basel , Lucerne University of Applied Sciences and Arts , Institute for Research in Biomedicine , IBM Research Zurich Lab , Swiss Institute of Bioinformatics , CSEM - Centre Suisse d'Electronique et Microtechnique , Corporates Switzerland , CERN , Hochschulmedizin Zürich , Université de Neuchâtel , Università della Svizzera italiana , Swiss National Science Foundation , University of Konstanz , University of Hamburg , University of Erlangen-Nuremberg , University of Cologne , Universität zu Lübeck , Universität Ulm , Universität der Bundeswehr München , TU Dresden , TU Darmstadt , TU Berlin , Technische Universität Hamburg , Max Planck Society , Otto Von Guericke Universitat, Magdeburg , RWTH Aachen University , Ludwig Maximilians Universiy Munich , Humboldt-Universität zu Berlin , European Molecular Biology Laboratory (EMBL) , Eberhard Karls Universität Tübingen , Max Delbruck Center for Molecular Medicine (MDC) , Technische Universität München , Imperial College London , National Institute for Medical Research , Royal College of Art , UCL - University College London , University of Aberdeen , University of Cambridge , University of Manchester , University of Nottingham , University of Oxford , University of Leeds , Delft University of Technology , Maastricht Science Programme , Radboud University Nijmegen , Utrecht University
Organization Sensory-Motor Systems Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Semester Project / Internship: Anomaly Detection for Biosignals Processing for Robots in Healthcare
Modern wearable and nearable sensors allow for continuous 24-hour health monitoring. In clinical settings, such a richness of data is highly desirable to closely monitor the health status of patients, however, often not exploited due to the lack of common interfaces and the overall complexity of integrating the devices into the clinical routine. Moreover, future treatment devices could benefit from interfacing with these sensors through a common interface to perform closed-loop interventions. In this project, you will develop a novel time-series online synchronization and communication agent adaptable to data changes for interfacing wearable, nearable sensors data with robots in an intelligent system. Together with a research team in Japan, this work will be implemented at several clinical settings.
Keywords
Data system architecture, Robot communication, wearable sensors, Healthcare, wearables, rehabilitation, Machine Learning Models,
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Semester Project , Collaboration , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-11-29 , Earliest start: 2024-03-01 , Latest end: 2024-12-01
Applications limited to CERN , Berner Fachhochschule , EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , Empa , Eawag , IBM Research Zurich Lab , Hochschulmedizin Zürich , Friedrich Miescher Institute , Forschungsinstitut für biologischen Landbau (FiBL) , Paul Scherrer Institute , NCCR Democracy , Lucerne University of Applied Sciences and Arts , Institute for Research in Biomedicine , Swiss National Science Foundation , Swiss Institute of Bioinformatics , Swiss Federal Institute for Forest, Snow and Landscape Research , University of Zurich , University of St. Gallen , University of Lucerne , University of Lausanne , University of Geneva , University of Fribourg , University of Berne , University of Basel , Université de Neuchâtel , Zurich University of the Arts , Zurich University of Applied Sciences , Wyss Translational Center Zurich , Institute of Robotics and Intelligent Systems D-MAVT , Corporates Switzerland , Department of Quantitative Biomedicine , Università della Svizzera italiana , NGOs Switzerland , CSEM - Centre Suisse d'Electronique et Microtechnique , TU Dresden , TU Darmstadt , Technische Universität München , Technische Universität Hamburg , RWTH Aachen University , Universität zu Lübeck , TU Berlin , Max Planck Society , University of Oxford , UCL - University College London , Imperial College London , University of Leeds , University of Cambridge , Delft University of Technology , Politecnico di Milano , The University of Tokyo , Tokyo Institute of Technology
Organization Sensory-Motor Systems Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
DynaGELLO: A Low Cost "Puppet" Teleoperation System for the DynaArm
The GELLO system proposed in [1] is a low-cost “puppet” robot arm that is used to teleoperate a larger, main robot arm. This project aims to adapt this open source design to enable teleoperation of the DynaArm, which is a robot manipulator arm custom designed by the Robotic Systems Lab to be mounted on the ANYmal quadruped platform. Such a system provides a simplification over the existing DynaArm teleoperation interface consisting of a second identical DynaArm used purely as a human interface device [2], which may be an unnecessarily expensive and cumbersome solution. The system developed may have applications in remote teleoperation for industrial inspection or disaster response scenarios, as well as providing an interface for training imitation learning models, which may optionally be explored as time permits. [1] Wu, Philip et al. "GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators". arXiv preprint (2024) [2] Fuchioka, Yuni et al. AIRA Challenge: Teleoperated Mobile Manipulation for Industrial Inspection. Youtube Video. (2024)
Keywords
Robotics, Teleoperation, Manipulation, Imitation Learning
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Published since: 2024-11-27 , Earliest start: 2025-01-06 , Latest end: 2025-07-31
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Fuchioka Yuni
Topics Information, Computing and Communication Sciences , Engineering and Technology
Pretraining for RL
This project addresses sampling inefficiency in classical reinforcement learning by exploring smart weight initialization. Inspired by computer vision, we aim to enhance learning across different hardware (cross embodiment) and skills (cross skills) using pre-trained representations, reducing training times and potentially improving the overall effectiveness of reinforcement learning policies.
Keywords
reinforcement learning weight initialization cross embodiment cross skills
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Master Thesis
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Published since: 2024-11-26 , Earliest start: 2024-09-01 , Latest end: 2025-03-31
Organization Robotic Systems Lab
Hosts Portela Tifanny , Cramariuc Andrei
Topics Information, Computing and Communication Sciences , Engineering and Technology
Periodic Motion Priors for General Quadruped Locomotion Learning
In recent years, advancements in reinforcement learning have achieved remarkable success in quadruped locomotion tasks. Despite their similar structural designs, quadruped robots often require uniquely tailored reward functions for effective motion pattern development, limiting the transferability of learned behaviors across different models. This project proposes to bridge this gap by developing a unified, continuous latent representation of quadruped motions applicable across various robotic platforms. By mapping these motions onto a shared latent space, the project aims to create a versatile foundation that can be adapted to downstream tasks for specific robot configurations.
Keywords
representation learning, periodic autoencoders, policy modulating trajectory generators
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Master Thesis
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Published since: 2024-11-26
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Miki Takahiro
Topics Information, Computing and Communication Sciences , Engineering and Technology
Lifelike Agility on ANYmal by Learning from Animals
The remarkable agility of animals, characterized by their rapid, fluid movements and precise interaction with their environment, serves as an inspiration for advancements in legged robotics. Recent progress in the field has underscored the potential of learning-based methods for robot control. These methods streamline the development process by optimizing control mechanisms directly from sensory inputs to actuator outputs, often employing deep reinforcement learning (RL) algorithms. By training in simulated environments, these algorithms can develop locomotion skills that are subsequently transferred to physical robots. Although this approach has led to significant achievements in achieving robust locomotion, mimicking the wide range of agile capabilities observed in animals remains a significant challenge. Traditionally, manually crafted controllers have succeeded in replicating complex behaviors, but their development is labor-intensive and demands a high level of expertise in each specific skill. Reinforcement learning offers a promising alternative by potentially reducing the manual labor involved in controller development. However, crafting learning objectives that lead to the desired behaviors in robots also requires considerable expertise, specific to each skill.
Keywords
learning from demonstrations, imitation learning, reinforcement learning
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Master Thesis
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Published since: 2024-11-26
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Klemm Victor
Topics Information, Computing and Communication Sciences
Pushing the Limit of Quadruped Running Speed with Autonomous Curriculum Learning
The project aims to explore curriculum learning techniques to push the limits of quadruped running speed using reinforcement learning. By systematically designing and implementing curricula that guide the learning process, the project seeks to develop a quadruped controller capable of achieving the fastest possible forward locomotion. This involves not only optimizing the learning process but also ensuring the robustness and adaptability of the learned policies across various running conditions.
Keywords
curriculum learning, fast locomotion
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Master Thesis
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Published since: 2024-11-26
Organization Robotic Systems Lab
Hosts Li Chenhao , Bagatella Marco , Li Chenhao , Li Chenhao , Li Chenhao
Topics Engineering and Technology
Leveraging Human Motion Data from Videos for Humanoid Robot Motion Learning
The advancement in humanoid robotics has reached a stage where mimicking complex human motions with high accuracy is crucial for tasks ranging from entertainment to human-robot interaction in dynamic environments. Traditional approaches in motion learning, particularly for humanoid robots, rely heavily on motion capture (MoCap) data. However, acquiring large amounts of high-quality MoCap data is both expensive and logistically challenging. In contrast, video footage of human activities, such as sports events or dance performances, is widely available and offers an abundant source of motion data. Building on recent advancements in extracting and utilizing human motion from videos, such as the method proposed in WHAM (refer to the paper "Learning Physically Simulated Tennis Skills from Broadcast Videos"), this project aims to develop a system that extracts human motion from videos and applies it to teach a humanoid robot how to perform similar actions. The primary focus will be on extracting dynamic and expressive motions from videos, such as soccer player celebrations, and using these extracted motions as reference data for reinforcement learning (RL) and imitation learning on a humanoid robot.
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Published since: 2024-11-26
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 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: 2024-11-26
Organization ETH Competence Center - ETH AI Center
Hosts He Junzhe , Li Chenhao , Li Chenhao
Topics Information, Computing and Communication Sciences
Continuous Skill Learning with Fourier Latent Dynamics
In recent years, advancements in reinforcement learning have achieved remarkable success in teaching robots discrete motor skills. However, this process often involves intricate reward structuring and extensive hyperparameter adjustments for each new skill, making it a time-consuming and complex endeavor. This project proposes the development of a skill generator operating within a continuous latent space. This innovative approach contrasts with the discrete skill learning methods currently prevalent in the field. By leveraging a continuous latent space, the skill generator aims to produce a diverse range of skills without the need for individualized reward designs and hyperparameter configurations for each skill. This method not only simplifies the skill generation process but also promises to enhance the adaptability and efficiency of skill learning in robotics.
Keywords
representation learning, periodic autoencoders, learning from demonstrations, policy modulating trajectory generators
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Master Thesis
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Published since: 2024-11-26
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Rudin Nikita
Topics Information, Computing and Communication Sciences , Engineering and Technology
Handstands with a quadruped robot
Doing handstands (one arm and two legs) with a quadrupedal robot equipped with an arm using reinforcement learning. After getting into a stable upright position the next step will also be locomoting in this tripod configuration.
Keywords
reinforcement learning agile control quadrupedal robots
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Master Thesis
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Published since: 2024-11-26 , Earliest start: 2024-12-01
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Cramariuc Andrei
Topics Information, Computing and Communication Sciences , Engineering and Technology
DynaHead++: Audio-visual telepresence system for ANYmal
The DynaHead system created at the Robotic Systems Lab enables intuitive telepresence, giving operators depth perception from a stereo camera whose pan-and-tilt motion is controlled through a Virtual Reality (VR) goggle that is worn by the operator. The goal of the project is to further advance this system to enable increased telepresence capabilities. In particular, the system proposed in [1] represents a more advanced version of the concept, providing audio feedback through microphones and the VR goggle’s built-in speaker system, as well as full 6 Degree of Freedom (DOF) motions of the camera compared to the DynaArm’s pan/tilt. We would like to similarly achieve immersive remote telepresence for ANYmal through the addition of directional audio feedback, as well as through additional optional extensions to the system. [1] Lenz, Christian et al. "NimbRo wins ANA Avatar XPRIZE Immersive Telepresence Competition: Human-Centric Evaluation and Lessons Learned". SORO, pp.1-25, (2023)
Keywords
Teleoperation, Telepresence, Virtual Reality, Robotics
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Published since: 2024-11-26 , Earliest start: 2025-01-06 , Latest end: 2025-07-31
Applications limited to ETH Zurich
Organization Robotic Systems Lab
Hosts Fuchioka Yuni , Wilder-Smith Max
Topics Information, Computing and Communication Sciences , Engineering and Technology
Universal Humanoid Motion Representations for Expressive Learning-based Control
Recent advances in physically simulated humanoids have broadened their application spectrum, including animation, gaming, augmented and virtual reality (AR/VR), and robotics, showcasing significant enhancements in both performance and practicality. With the advent of motion capture (MoCap) technology and reinforcement learning (RL) techniques, these simulated humanoids are capable of replicating extensive human motion datasets, executing complex animations, and following intricate motion patterns using minimal sensor input. Nevertheless, generating such detailed and naturalistic motions requires meticulous motion data curation and the development of new physics-based policies from the ground up—a process that is not only labor-intensive but also fraught with challenges related to reward system design, dataset curation, and the learning algorithm, which can result in unnatural motions. To circumvent these challenges, researchers have explored the use of latent spaces or skill embeddings derived from pre-trained motion controllers, facilitating their application in hierarchical RL frameworks. This method involves training a low-level policy to generate a representation space from tasks like motion imitation or adversarial learning, which a high-level policy can then navigate to produce latent codes that represent specific motor actions. This approach promotes the reuse of learned motor skills and efficient action space sampling. However, the effectiveness of this strategy is often limited by the scope of the latent space, which is traditionally based on specialized and relatively narrow motion datasets, thus limiting the range of achievable behaviors. An alternative strategy involves employing a low-level controller as a motion imitator, using full-body kinematic motions as high-level control signals. This method is particularly prevalent in motion tracking applications, where supervised learning techniques are applied to paired input data, such as video and kinematic data. For generative tasks without paired data, RL becomes necessary, although kinematic motion presents challenges as a sampling space due to its high dimensionality and the absence of physical constraints. This necessitates the use of kinematic motion latent spaces for generative tasks and highlights the limitations of using purely kinematic signals for tasks requiring interaction with the environment or other agents, where understanding of interaction dynamics is crucial. We would like to extend the idea of creating a low-level controller as a motion imitator to full-body motions from real-time expressive kinematic targets.
Keywords
representation learning, periodic autoencoders
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Master Thesis
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Published since: 2024-11-26
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao
Topics Information, Computing and Communication Sciences , Engineering and Technology
Humanoid Locomotion Learning and Finetuning from Human Feedback
In the burgeoning field of deep reinforcement learning (RL), agents autonomously develop complex behaviors through a process of trial and error. Yet, the application of RL across various domains faces notable hurdles, particularly in devising appropriate reward functions. Traditional approaches often resort to sparse rewards for simplicity, though these prove inadequate for training efficient agents. Consequently, real-world applications may necessitate elaborate setups, such as employing accelerometers for door interaction detection, thermal imaging for action recognition, or motion capture systems for precise object tracking. Despite these advanced solutions, crafting an ideal reward function remains challenging due to the propensity of RL algorithms to exploit the reward system in unforeseen ways. Agents might fulfill objectives in unexpected manners, highlighting the complexity of encoding desired behaviors, like adherence to social norms, into a reward function. An alternative strategy, imitation learning, circumvents the intricacies of reward engineering by having the agent learn through the emulation of expert behavior. However, acquiring a sufficient number of high-quality demonstrations for this purpose is often impractically costly. Humans, in contrast, learn with remarkable autonomy, benefiting from intermittent guidance from educators who provide tailored feedback based on the learner's progress. This interactive learning model holds promise for artificial agents, offering a customized learning trajectory that mitigates reward exploitation without extensive reward function engineering. The challenge lies in ensuring the feedback process is both manageable for humans and rich enough to be effective. Despite its potential, the implementation of human-in-the-loop (HiL) RL remains limited in practice. Our research endeavors to significantly lessen the human labor involved in HiL learning, leveraging both unsupervised pre-training and preference-based learning to enhance agent development with minimal human intervention.
Keywords
reinforcement learning from human feedback, preference learning
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Master Thesis
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Published since: 2024-11-26
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Chen Xin , Li Chenhao
Topics Information, Computing and Communication Sciences , Engineering and Technology
Online Safe Locomotion Learning in the Wild
Reinforcement learning (RL) can potentially solve complex problems in a purely data-driven manner. Still, the state-of-the-art in applying RL in robotics, relies heavily on high-fidelity simulators. While learning in simulation allows to circumvent sample complexity challenges that are common in model-free RL, even slight distribution shift ("sim-to-real gap") between simulation and the real system can cause these algorithms to easily fail. Recent advances in model-based reinforcement learning have led to superior sample efficiency, enabling online learning without a simulator. Nonetheless, learning online cannot cause any damage and should adhere to safety requirements (for obvious reasons). The proposed project aims to demonstrate how existing safe model-based RL methods can be used to solve the foregoing challenges.
Keywords
safe mode-base RL, online learning, legged robotics
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Master Thesis
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Published since: 2024-11-26
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Engineering and Technology
Autonomous Curriculum Learning for Increasingly Challenging Tasks
While the history of machine learning so far largely encompasses a series of problems posed by researchers and algorithms that learn their solutions, an important question is whether the problems themselves can be generated by the algorithm at the same time as they are being solved. Such a process would in effect build its own diverse and expanding curricula, and the solutions to problems at various stages would become stepping stones towards solving even more challenging problems later in the process. Consider the realm of legged locomotion: Training a robot via reinforcement learning to track a velocity command illustrates this concept. Initially, tracking a low velocity is simpler due to algorithm initialization and environmental setup. By manually crafting a curriculum, we can start with low-velocity targets and incrementally increase them as the robot demonstrates competence. This method works well when the difficulty correlates clearly with the target, as with higher velocities or more challenging terrains. However, challenges arise when the relationship between task difficulty and control parameters is unclear. For instance, if a parameter dictates various human dance styles for the robot to mimic, it's not obvious whether jazz is easier than hip-hop. In such scenarios, the difficulty distribution does not align with the control parameter. How, then, can we devise an effective curriculum? In the conventional RSL training setting for locomotion over challenging terrains, there is also a handcrafted learning schedule dictating increasingly hard terrain levels but unified with multiple different types. With a smart autonomous curriculum learning algorithm, are we able to overcome separate terrain types asynchronously and thus achieve overall better performance or higher data efficiency?
Keywords
curriculum learning, open-ended learning, self-evolution, progressive task solving
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Master Thesis
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Published since: 2024-11-26
Organization Robotic Systems Lab
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Bagatella Marco , Li Chenhao
Topics Engineering and Technology
Humanoid Locomotion Learning with Human Motion Priors
Humanoid robots, designed to replicate human structure and behavior, have made significant strides in kinematics, dynamics, and control systems. Research aims to develop robots capable of performing tasks in human-centric settings, from simple object manipulation to navigating complex terrains. Reinforcement learning (RL) has proven to be a powerful method for enabling robots to learn from their environment, enhancing their performance over time without explicit programming for every possible scenario. In the realm of humanoid robotics, RL is used to optimize control policies, adapt to new tasks, and improve the efficiency and safety of human-robot interactions. However, one of the primary challenges is the high dimensionality of the action space, where handcrafted reward functions fall short of generating natural, lifelike motions. Incorporating motion priors into the learning process of humanoid robots addresses these challenges effectively. Motion priors can significantly reduce the exploration space in RL, leading to faster convergence and reduced training time. They ensure that learned policies prioritize stability and safety, reducing the risk of unpredictable or hazardous actions. Additionally, motion priors guide the learning process towards more natural, human-like movements, improving the robot's ability to perform tasks intuitively and seamlessly in human environments. Therefore, motion priors are crucial for efficient, stable, and realistic humanoid locomotion learning, enabling robots to better navigate and interact with the world around them.
Keywords
motion priors, humanoid, reinforcement learning, representation learning
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Master Thesis
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Published since: 2024-11-26
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Information, Computing and Communication Sciences
Learning World Models for Legged Locomotion
Model-based reinforcement learning learns a world model from which an optimal control policy can be extracted. Understanding and predicting the forward dynamics of legged systems is crucial for effective control and planning. Forward dynamics involve predicting the next state of the robot given its current state and the applied actions. While traditional physics-based models can provide a baseline understanding, they often struggle with the complexities and non-linearities inherent in real-world scenarios, particularly due to the varying contact patterns of the robot's feet with the ground. The project aims to develop and evaluate neural network-based models for predicting the dynamics of legged environments, focusing on accounting for varying contact patterns and non-linearities. This involves collecting and preprocessing data from various simulation environment experiments, designing neural network architectures that incorporate necessary structures, and exploring hybrid models that combine physics-based predictions with neural network corrections. The models will be trained and evaluated on prediction autoregressive accuracy, with an emphasis on robustness and generalization capabilities across different noise perturbations. By the end of the project, the goal is to achieve an accurate, robust, and generalizable predictive model for the forward dynamics of legged systems.
Keywords
forward dynamics, non-smooth dynamics, neural networks, model-based reinforcement learning
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Master Thesis
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Published since: 2024-11-26
Organization Robotic Systems Lab
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Klemm Victor , Li Chenhao
Topics Engineering and Technology
DigGPT: Large Language Models for Excavation Planning
Large language models (LLMs) have shown the first sparks of artificial general intelligence. We want to test if GPT 4.0 can solve excavation planning problems.
Keywords
GPT, Large Language Models, Robotics, Deep Learning, Reinforcement Learning
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Published since: 2024-11-21 , Earliest start: 2025-01-01 , Latest end: 2025-08-31
Organization Robotic Systems Lab
Hosts Terenzi Lorenzo
Topics Engineering and Technology
Brain plasticity after spinal cord injury
In this project we are interested in identifying how finger-specific population receptive fields change in the primary somatosensory cortex following a tetraplegic spinal cord injury. Following a tetraplegic spinal cord injury, patients experience a (partial) loss of sensory and motor function of their limbs and torso. This is thought to lead to extensive reorganisation in brain areas containing detailed map-like body representations (e.g. the primary somatosensory cortex), such that neighbouring body-part representations ‘invade’ the area deprived of input and/or output. However, using a novel experimental approach we recently demonstrated, using an attempted movement paradigm and functional MRI (fMRI), that the functional representation of patients’ hands is preserved even decades after injury (Kikkert et al., 2021 eLife). In this project, we want to dive into this already existing fMRI dataset more deeply. As receptive field sizes have been thought to be dependent on daily sensory experience, it is likely that finger-movement related receptive field sizes may change after tetraplegia, even though the functional representation of the hand is preserved. In this project we aim to tease these two processes apart.
Keywords
analysis, neuroimaging, MRI, brain, spinal cord injury
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Master Thesis
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Published since: 2024-11-21 , Earliest start: 2025-01-06
Applications limited to ETH Zurich , University of Zurich , Department of Computer Science , Department of Biology , Department of Health Sciences and Technology
Organization Neural Control of Movement Lab
Hosts Kikkert Sanne
Topics Medical and Health Sciences
Pre-clinical mechanical evaluation of a novel spinal implant
Background: 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 semester project or master thesis. In this role, you will gain insight into the spinal surgery process, receive input from surgeons, and contribute to the mechanical design and testing of the implant. Objectives: • Perform the CT scan on animal vertebrae • Evaluate the influence of implant placement/location variability • Mechanical testing on the implant • Develop surgical tools if needed Your Profile: • Strong knowledge in mechanical design and drawing skills. • Hands-on and detail-oriented. • Experience with SolidWorks or Fusion 360, as well as Python or Matlab. Timeframe: Spring semester in 2025
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Published since: 2024-11-20 , Earliest start: 2025-01-01 , Latest end: 2025-08-31
Organization Bone Pathologies and Treatment
Hosts Du Xiaoyu
Topics Medical and Health Sciences
Untersuchung einer innovativen Bürodrehstuhlmechanik: Analyse des Sitzverhaltens mittels IMUs
In dieser Nutzerstudie soll eine neue Bürostuhlmechanik untersucht werden, die dynamisches Sitzverhalten fördern soll. Ziel der Studie ist es, die Nutzung des Stuhls im Büroalltag zu analysieren und die Ergebnisse mit denen herkömmlicher Bürostühle zu vergleichen. Dabei werden Bewegungssensoren (IMUs) eingesetzt, um detaillierte Bewegungsdaten von Sitz- und Rückenlehnen zu erfassen. Die Arbeit bietet spannende Einblicke in die Anwendung von biomechanischen Messmethoden und die Analyse von Bewegungsdaten im Büroalltag.
Keywords
sitting behavior, IMU, low back pain, dynamic sitting
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Published since: 2024-11-19 , Earliest start: 2024-11-25 , Latest end: 2025-08-31
Organization Sports Biomechanics Group
Hosts Zemp Roland, Dr.
Topics Medical and Health Sciences , Engineering and Technology
Kinematic Modeling of a Robotic Arthroscope
This master thesis focuses on the kinematic modeling of a 4-DoF hand-held robotic arthroscope. The main challenge will be to find fast and accurate forward (and backward) kinematics for a non-holonomic robot.
Keywords
Kinematic Modeling; Surgery; Arthroscope; Robot; Laser; Inverse Kinematics
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Published since: 2024-11-14 , Earliest start: 2025-01-01 , Latest end: 2025-12-31
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Organization Sensory-Motor Systems Lab
Hosts Sommerhalder Michael
Topics Engineering and Technology
Multisensory assessment of physiological markers during neural stimulation for stroke rehabilitation
Project goal is to assess outcomes of a non-invasive brain stimulator for future application in stroke rehabilitation. This will involve using an exciting novel method of brain stimulation together with simultaneous multisensory recordings of various physiological parameters, including heart rate, galvanic skin response, pupillometry and electroencephalogram (EEG). The results of the project will help develop brain stimulation protocols that elicit meaningful neural responses in healthy subjects, and in stroke patients.
Keywords
neural stimulation, neural biomarkers, neurophysiology, physiology, neuroscience, EEG, pupillometry
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Published since: 2024-11-11 , Earliest start: 2023-06-01
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius , Donegan Dane
Topics Engineering and Technology
Real-time control of neural stimulation for stroke patients.
Real-time analysis of movement kinematics can benefit multiple different strategies in rehabilitation after stroke, including allowing closed-loop brain stimulation. Use of inertial measurement units (IMUs) allows detection of movement and extraction of kinematic features, but application in real-time remains challenging. This project will develop algorithms for real-time movement data analysis and feature extraction in typical rehabilitation tasks and general real-life movements. In turn, these algorithms will be applied to control novel brain stimulation approaches in stroke neurorehabilitation.
Keywords
Inertial measurement unit, IMU, movement tracking, machine learning, real-time, signal processing
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Published since: 2024-11-11 , Earliest start: 2023-05-09
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius , Donegan Dane
Topics Engineering and Technology
Mechanistic Evaluation of taVNS in Motor Adaptation for Stroke Rehabilitation
This study aims to evaluate the mechanisms by which transauricular vagus nerve stimulation (taVNS) may facilitate motor learning and adaptation, focusing on reticulospinal tract (RST) activation. A set of assessments will be used to build a comprehensive neurophysiological profile, providing insights relevant to developing taVNS-based clinical interventions. In this role, you will engage in study design, data collection, and analysis, gaining hands-on experience in mechanistic neurophysiological research with direct clinical applications.
Keywords
Transauricular vagus nerve stimulation (taVNS) Reticulospinal tract (RST) Motor learning StartReact paradigm Stroke rehabilitation Neurophysiology Multisite EMG Motor pathway activation Clinical neurostimulation
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-11-11 , Earliest start: 2024-11-24 , Latest end: 2025-10-26
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius
Topics Medical and Health Sciences , Engineering and Technology
Development of flexible, biocompatible electrodes for vagus nerve stimulation therapy
Stroke is the most common cause of disability and as many as 14 million people suffer a stroke every year. Typical therapy today is focused on physical exercises and rarely combats the core of the problem - the interactions between the motor actions and the activity patterns of the injured brain. Therefore, we are developing a cutting-edge medical device that helps combine physical therapy with a non-invasive brain stimulation. This is expected to boost recovery after stroke and make this advanced neuro-therapy accessible to millions of people who need it.
Keywords
Biocompatibility Conductive Materials Vagus Nerve Stimulation (VNS) Transcutaneous VNS (tVNS) Stroke Rehabilitation Therapy Medical Device Engineering Electrode Design and Prototyping Materials Science Biomedical Engineering Neurorehabilitation Clinical Usability Patient-Centred Design
Labels
Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2024-11-11 , Earliest start: 2024-11-24 , Latest end: 2025-10-31
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius
Topics Medical and Health Sciences , Engineering and Technology