Student Projects
How to apply
To apply, please send your CV, your Ms and Bs transcripts by email to all the contacts indicated below the project description. Do not apply on SiROP . Since Prof. Davide Scaramuzza is affiliated with ETH, there is no organizational overhead for ETH students. Custom projects are occasionally available. If you would like to do a project with us but could not find an advertized project that suits you, please contact Prof. Davide Scaramuzza directly to ask for a tailored project (sdavide at ifi.uzh.ch).
Upon successful completion of a project in our lab, students may also have the opportunity to get an internship at one of our numerous industrial and academic partners worldwide (e.g., NASA/JPL, University of Pennsylvania, UCLA, MIT, Stanford, ...).
Low Latency Occlusion-aware Object Tracking - Available
Description: In this project, we will develop a low-latency, robust to occlusion, object tracker. Three main paradigms exist in the literature to perform object tracking: Tracking-by-detection, Tracking-by-regression, and Tracking-by-attention. We will start with a deep literature review to evaluate the current solutions to our end goal of being fast and robust to occlusion. Starting from the conclusions of this study, we will design a novel tracker that can achieve our goal. In addition to RGB images, we will investigate other sensor modalities such as inertial measurement units and event cameras. We will use the Meta Aria smart glasses (Meta Aria Project: https://www.projectaria.com/).
Goal: Develop a low-latency object tracker that is robust to occlusions. We look for students with a strong computer vision background and familiar with common software tools used in Deep Learning (for example, PyTorch or TensorFlow).
Contact Details: Giovanni Cioffi [cioffi (at) ifi (dot) uzh (dot) ch], Roberto Pellerito [rpellerito (at) ifi (dot) uzh (dot) ch], Prof. Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch]
Thesis Type: Master Thesis
Reading in the Dark - Available
Description: In this project, we will investigate the use of event cameras for optical character recognition (OCR) in low light. The current OCR methods require RGB (or gray-scale) images as input. Their performance degrades rapidly in low light conditions hindering the application in dim light scenarios as in a restaurant or reading a book at night at home. The project will involve designing an event-based OCR algorithm and evaluating such an algorithm on a self-recorded dataset using an event camera and smart glasses (Meta Aria Project: https://www.projectaria.com/).
Goal: Develop an event-based OCR algorithm and test it in dim light conditions. We look for students with a strong computer vision background and familiar with common software tools used in Deep Learning (for example, PyTorch or TensorFlow).
Contact Details: Giovanni Cioffi [cioffi (at) ifi (dot) uzh (dot) ch], Roberto Pellerito[rpellerito (at) ifi (dot) uzh (dot) ch], Prof. Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch]
Thesis Type: Master Thesis
Electrical Flow-Based Graph Embeddings for Event-based Vision and other downstream tasks - Available
Description: Besides RPG, this project will be co-supervised by Simon Meierhans (from Alg. & Opt. group at ETH) and prof. Siddhartha Mishra. This project explores a novel approach to graph embeddings using electrical flow computations. By leveraging the efficiency of solving systems of linear equations and some properties of electrical flows, we aim to develop a new method for creating low-dimensional representations of graphs. These embeddings have the potential to capture unique structural and dynamic properties of networks. The project will investigate how these electrical flow-based embeddings can be utilized in various downstream tasks such as node classification, link prediction, graph classification and event-based vision tasks.
Goal: The primary goal of this project is to design, implement, and evaluate a graph embedding technique based on electrical flow computations. The student will develop algorithms to compute these embeddings efficiently, compare them with existing graph embedding methods, and apply them to real-world network datasets. The project will also explore the effectiveness of these embeddings in downstream machine learning tasks. Applicants should have a strong background in graph theory, linear algebra, and machine learning, as well as proficiency in Python and ideally experience with graph processing libraries like NetworkX or graph-tool.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master) to Nikola Zubic (zubic@ifi.uzh.ch), Simon Meierhans (simon.meierhans@inf.ethz.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
Thesis Type: Master Thesis
Vision-Based Agile Aerial Transportation - Available
Description: Transporting loads with drones is often constrained by traditional control systems that rely on predefined flight paths, GPS, or external motion capture systems. These methods limit a drone's adaptability and responsiveness, particularly in dynamic or cluttered environments. Vision-based control has the potential to revolutionize aerial transportation by enabling drones to perceive and respond to their surroundings in real-time. Imagine a drone that can swiftly navigate through complex environments and deliver payloads with precision using only onboard vision sensors.
Goal: This project aims to develop a vision-based control system for drones capable of agile and efficient aerial transportation. The system will leverage real-time visual input to dynamically adapt to environmental conditions, navigate obstacles, and manage load variations with reinforcement or imitation learning.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Ismail Geles [geles (at) ifi (dot) uzh (dot) ch], Leonard Bauersfeld [bauersfeld (at) ifi (dot) uzh (dot) ch], Angel Romero [roagui (at) ifi (dot) uzh (dot) ch]
Thesis Type: Semester Project / Master Thesis
Leveraging Long Sequence Modeling for Drone Racing - Available
Description: Recent advancements in machine learning have highlighted the potential of Long Sequence Modeling as a powerful approach for handling complex temporal dependencies, positioning it as a compelling alternative to traditional Transformer-based models. In the context of drone racing, where split-second decision-making and precise control are of greatest importance, Long Sequence Modeling can offer significant improvements. These models are adept at capturing intricate state dynamics and handling continuous-time parameters, providing the flexibility to adapt to varying time steps essential for high-speed navigation and obstacle avoidance. This project aims to bridge this gap by investigating the application of Long Sequence Modeling techniques in RL to develop advanced autonomous drone racing systems. The ultimate goal is to improve autonomous drones' performance, reliability, and adaptability in competitive racing scenarios.
Goal: Develop a Reinforcement Learning framework based on Long Sequence Modeling tailored for drone racing. Simulate the framework to evaluate its performance in controlled environments. Conduct a comprehensive analysis of the framework’s effectiveness in handling long sequences and dynamic racing scenarios. Ideally, the optimized model should be deployed in real-world drone racing settings to validate its practical applicability and performance.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master) to Nikola Zubic (zubic@ifi.uzh.ch), Angel Romero Aguilar (roagui@ifi.uzh.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
Thesis Type: Master Thesis
What can Large Language Models offer to Event-based Vision? - Available
Description: Event-based vision algorithms process visual changes in an asynchronous manner akin to how biological visual systems function, while large language models (LLMs) specialize in parsing and generating human-like text. This project aims to explore the intersection of Large Language Models (LLMs) and Event-based Vision, leveraging the unique capabilities of each domain to create a symbiotic framework. By marrying the strengths of both technologies, the initiative aims to develop a novel, more robust paradigm that excels in challenging conditions.
Goal: The primary objective is to devise methodologies that synergize the capabilities of LLMs with Event-Based Vision systems. We intend to address identified shortcomings in existing paradigms by leveraging the inferential strengths of LLMs. Rigorous evaluations will be conducted to validate the efficacy of the integrated system under various challenging conditions.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master) to Nikola Zubic (zubic@ifi.uzh.ch), Nico Messikommer (nmessi@ifi.uzh.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
Thesis Type: Semester Project / Master Thesis
Neural Architecture Knowledge Transfer for Event-based Vision - Available
Description: Processing the sparse and asynchronous data from event-based cameras presents significant challenges. Transformer-based models have achieved remarkable results in sequence modeling tasks, including event-based vision, due to their powerful representation capabilities. Despite their success, their high computational complexity and memory demands make them impractical for deployment on resource-constrained devices typical in real-world applications. Recent advancements in efficient sequence modeling architectures offer promising alternatives that provide competitive performance with significantly reduced computational overhead. Recognizing that Transformers already demonstrate strong performance on event-based vision tasks, we aim to leverage their strengths while addressing efficiency concerns.
Goal: Study knowledge transfer techniques to transfer knowledge from complex Transformer models to simpler, more efficient models. Test the developed models on benchmark event-based vision tasks such as object recognition, optical flow estimation, and SLAM.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master) to Nikola Zubic (zubic@ifi.uzh.ch), Giovanni Cioffi (cioffi@ifi.uzh.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
Thesis Type: Master Thesis
Improving Event-Based Vision with Energy-Efficient Neural Networks - Available
Description: Event-based cameras, also known as neuromorphic vision sensors, capture visual information through asynchronous pixel-level brightness changes, offering high temporal resolution, low latency, and a wide dynamic range. These characteristics make them ideal for applications requiring rapid response times and efficient data processing. However, deploying deep learning models on resource-constrained devices remains challenging due to computational overhead and energy consumption. This project explores novel approaches to developing energy-efficient neural networks tailored for event-based vision tasks. By designing models that significantly reduce computational demands and memory footprint while maintaining high performance, we can make real-time processing on embedded hardware feasible. The focus will be on balancing training efficiency and model accuracy, minimizing energy consumption without sacrificing the quality of results.
Goal: Investigate existing energy-efficient neural network architectures that can be applied to event-based vision. Design and implement energy-efficient neural networks specifically for event-based vision tasks. Explore techniques to optimize model architectures for efficiency without compromising accuracy. Test the developed models on benchmark event-based datasets, such as N-Caltech101, N-CARS, and Neuromorphic ImageNet.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master) to Nikola Zubic (zubic@ifi.uzh.ch), Marco Cannici (cannici@ifi.uzh.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
Thesis Type: Semester Project / Master Thesis
Hybrid Spiking-Deep Neural Network System for Efficient Event-Based Vision Processing - Available
Description: Event cameras are innovative sensors that capture changes in a scene dynamically, unlike standard cameras that capture images at fixed intervals. They detect pixel-level brightness changes, providing high temporal resolution and low latency. This results in efficient data processing and reduced power consumption, typically just 1 mW. Spiking Neural Networks (SNNs) process information as discrete events or spikes, mimicking the brain's neural activity. They differ from standard Neural Networks (NNs) that process information continuously. SNNs are highly efficient in power consumption and well-suited for event-driven data from event cameras. In collaboration with SynSense, this project aims to integrate the rapid processing capabilities of SNNs with the advanced analytic powers of deep neural networks. By distilling higher-level features from raw event data, we aim to significantly reduce the volume of events needing further processing by traditional NNs, improving data quality and transmission efficiency. System will be tested on computer vision tasks like object detection and tracking, gesture recognition, and high-speed motion estimation.
Goal: The primary goal is to develop a hybrid system that combines Spiking Neural Networks (SNNs) and deep neural networks to process event data efficiently at the sensor level. We will demonstrate its versatility and effectiveness in various computer vision tasks. Rigorous testing in simulation will assess the impact on data quality and processing efficiency, followed by deployment on real hardware to evaluate real-world performance.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master) to Nikola Zubic (zubic@ifi.uzh.ch), Marco Cannici (cannici@ifi.uzh.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
Thesis Type: Master Thesis
Fast Object Detection Using Spiking Neural Networks and Event Cameras for Real-Time Scene Analysis - Available
Description: Spiking Neural Networks (SNNs) are renowned for their efficiency and their ability to encode fine-grained temporal patterns. Paired with event cameras, which operate asynchronously and capture changes in a scene with microsecond-level temporal resolution, this combination becomes a powerful tool for analyzing dynamic environments. Together, SNNs and event cameras are uniquely suited to tasks requiring rapid detection and response, as they excel in capturing and processing temporal dynamics with minimal latency. This project aims to harness these technologies to develop a system capable of detecting fast-moving objects in a scene at high speed and efficiency, addressing challenges such as high-speed motion and event data sparsity. We invite candidates with a strong interest in neural networks and event-based vision systems to apply. Familiarity with SNNs and event cameras is a plus. Relevant skills include experience with neural networks, programming proficiency (Python and/or C++), and a good foundation in computer vision and robotics.
Goal: The project, conducted in collaboration with SynSense, will explore the use of SNNs for detecting fast-moving objects in real-time using event camera data. The primary objectives include developing and optimizing SNN algorithms to process event streams with minimal delay, designing experiments to evaluate the system's performance in detecting and tracking dynamic objects, and comparing the approach to traditional methods in terms of accuracy, latency, and power efficiency. The system will be tested in simulation and on real-world scenarios.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Marco Cannici (cannici AT ifi DOT uzh DOT ch), Elie Aljalbout [aljalbout (at) ifi (dot) uzh (dot) ch], Nikola Zubic [zubic (at) ifi (dot) uzh (dot) ch], Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch].
Thesis Type: Semester Project / Master Thesis
Event-Augmented Image-Based Neural Networks for High-Accuracy, Low-Latency Visual Perception - Available
Description: Traditional image-based neural networks excel in tasks requiring rich spatial and texture information, but their low frame rates and high bandwidth requirements limit their temporal resolution. Conversely, event-based data provides high temporal resolution and sparse spatial data, making it ideal for rapid dynamic scene understanding, but lack in rich texture details. This project aims to combine these two modalities by building on state-of-the-art image-based networks and enhancing them with event data. By integrating the high temporal resolution of event streams with the detailed spatial information from traditional networks, the project will enable improved performance in real-time applications like semantic segmentation and object detection, validated on benchmarks such as the DSEC dataset. Additionally, it will explore challenges such as open-vocabulary recognition, allowing the system to adapt to objects and categories not explicitly included during training. We invite candidates with a passion for computer vision and neural networks to apply. Familiarity with event cameras is a plus. Relevant skills include proficiency in programming (Python and/or C++), experience in semantic segmentation and object detection, and a strong foundation in computer vision and machine learning.
Goal: The goal of the project is to design a hybrid framework that leverages pre-trained state-of-the-art image-based architectures and augments them with event camera data to achieve high accuracy and low latency in dynamic environments. The framework will be evaluated on key benchmarks for tasks like object detection and semantic segmentation, tackling challenges such as open-vocabulary recognition and classifying objects not explicitly seen during training, thereby broadening the system's applicability in real-world scenarios.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Marco Cannici (cannici AT ifi DOT uzh DOT ch), Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch].
Thesis Type: Semester Project / Master Thesis
Enhancing Vision-Based Drone Racing with Neural-based Rendering Augmentation Techniques - Available
Description: Autonomous drone racing presents a significant challenge, requiring drones to follow racetracks and pass through gates in minimal time with high precision. While reinforcement learning (RL) policies have achieved promising results in controlled environments, training generalizable policies that adapt to new, unseen environments remains an open problem. Vision-based policies are particularly susceptible to overfitting, such as in the detection of gates, where the network may rely on specific background features or textures from the training environment rather than learning robust gate detection. This project leverages recent advancements in data augmentation techniques and neural-based rendering to address these challenges, focusing on enhancing perception robustness and improving policy generalization for drone racing through tailored data augmentation methods. We invite candidates passionate about computer vision, and robotics to apply. Familiarity with neural rendering techniques and data augmentation is a plus. Relevant skills include proficiency in programming (Python and/or C++), experience with deep learning frameworks, and a strong foundation in computer vision and machine learning.
Goal: The project aims to develop advanced data augmentation strategies, incorporating state-of-the-art neural rendering techniques, to improve the generalization of vision-based policies to new environments. The effectiveness of the approach will be tested on real-world data, with a specific focus on enhancing the detection accuracy of existing gate detection pipelines in unknown environments.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Marco Cannici (cannici AT ifi DOT uzh DOT ch), Leonard Bauersfeld (bauersfeld AT ifi DOT uzh DOT ch), Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch].
Thesis Type: Semester Project / Master Thesis
Drones for search and rescue in disaster scenarios using RL - Available
Description: Natural disasters pose significant challenges to timely and effective emergency response, often requiring rapid assessment of hazardous areas that are difficult or dangerous for humans to access. Autonomous drones have the potential to revolutionize disaster response by providing a fast, efficient, and adaptable means of navigating these environments. To realize this potential, advanced training environments are essential to prepare drones for the complex realities of disaster scenarios. This project leverages cutting-edge research in drone racing and reinforcement learning to create a highly realistic simulation environment, accurately modeling damaged buildings and potential survivor locations.
Goal: This project aims to develop a sophisticated Reinforcement Learning (RL) environment to train autonomous drones for efficient disaster response operations. By leveraging insights from drone racing research, the project will focus on creating a highly realistic 3D simulation environment. The goal is to train drones to navigate these challenging environments, locate survivors swiftly, and make informed decisions in real-time.
Contact Details: Please send your CV and transcripts (bachelor and master), and any projects you have worked on that you find interesting to Angel Romero (roagui AT ifi DOT uzh DOT ch), Ismail Geles (geles AT ifi DOT uzh DOT ch), Jiaxu Xing (jixing AT ifi DOT uzh DOT ch), and Elie Aljalbout (aljalbout AT ifi DOT uzh DOT ch)
Thesis Type: Semester Project / Master Thesis
Leveraging Event Cameras for 3D Gaussian Splatting Reconstruction under fast motion - Available
Description: Building on the advancements of 3D Gaussian Splatting (3DGS) methods for scene reconstruction and synthesis, this project focuses on overcoming significant limitations that arise in scenarios involving fast camera motion or rapid object dynamics. Current 3DGS methods, while impressive under controlled conditions, struggle in two key areas: (1) inaccuracies in camera tracking within 3DGS-enabled SLAM systems and (2) degraded object reconstruction quality due to motion blur. These challenges are further amplified in scenes with low light or high dynamic range. Event cameras, with their high temporal resolution and robustness to motion blur, present an exciting opportunity to address these issues. By leveraging the asynchronous event streams provided by these cameras, we aim to enhance the performance of 3DGS methods in tracking and reconstruction tasks under challenging conditions. Applicants with expertise in programming, computer vision, and experience with machine learning frameworks (e.g., PyTorch) are invited to apply. Previous experience with 3DGS, NeRF and event-based cameras is a plus.
Goal: The project aims to develop a novel framework that integrates the high temporal resolution and motion blur robustness of event cameras with state-of-the-art 3D Gaussian Splatting (3DGS) techniques to enable accurate scene reconstruction and synthesis in challenging dynamic environments. Key objectives include enhancing 3DGS-based SLAM systems for improved camera tracking and addressing motion blur-induced degradation in object reconstruction quality. The framework will be benchmarked on tasks involving fast camera motion, rapid object dynamics, low-light scenarios, and high dynamic range scenes, demonstrating its robustness and scalability. The ultimate goal is to establish new methods for leveraging event cameras to advance 3DGS technologies, enabling high-quality reconstructions in real-world dynamic environments.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Marco Cannici (cannici AT ifi DOT uzh DOT ch), Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch].
Thesis Type: Semester Project / Master Thesis
Efficient Event Stream Encoding Using Spiking Neural Networks for Low-Latency Visual Processing - Available
Description: Spiking Neural Networks (SNNs) are highly efficient in terms of power consumption and latency, making them ideal for processing asynchronous data streams from event cameras. Despite these advantages, SNNs are challenging to train due to their spiking nature and are limited by the hardware constraints of neuromorphic chips, which can only support shallow networks, thus making hybrid SNN+ANN networks appealing. Additionally, event cameras, while excellent for capturing high-speed dynamic scenes, can generate millions of events per second under rapid motion, making it difficult to process such large volumes of data efficiently. This project aims to address these challenges by leveraging SNNs to compress raw event streams into compact representations for ANNs that maintain the sparsity and low-latency benefits of event-based data while retaining high accuracy for downstream tasks. We invite candidates passionate about computer vision or neuromorphic computing to apply. Familiarity with SNNs and event cameras is a plus. Relevant skills include proficiency in programming (Python and/or C++), experience with deep learning frameworks, and good foundation in computer vision.
Goal: The project, conducted in collaboration with SynSense, focuses on developing a robust SNN-based framework for compressing raw event streams. The compressed representations will enable efficient processing of large event volumes while preserving temporal and spatial fidelity. The framework will be benchmarked on multiple tasks, including classification, object detection, and optical flow prediction, to assess its accuracy, efficiency, and scalability. This research will establish new methods for exploiting SNNs in dynamic environments, demonstrating their potential to revolutionize event-based vision systems.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Marco Cannici (cannici AT ifi DOT uzh DOT ch), Elie Aljalbout [aljalbout (at) ifi (dot) uzh (dot) ch], Nikola Zubic [zubic (at) ifi (dot) uzh (dot) ch], Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch].
Thesis Type: Semester Project / Master Thesis
Distilling Large World Models for Real-Time Mobile Robot Control - Available
Description: This project aims to distill large, complex world models into lightweight, efficient versions capable of fast inference, enabling real-time feedback control for mobile robots. Large-scale world models that allow controlled image generation often suffer from high computational demands, limiting their utility on resource-constrained platforms. By leveraging model distillation techniques, knowledge from a pre-trained large model can be transferred to a smaller one through teacher-student learning, optimized loss functions, and methods like pruning, quantization, or neural architecture search (NAS). The distilled model will be deployed on a mobile robot to evaluate its real-world performance in terms of latency, energy efficiency, and task success rates. This project will follow a structured timeline: starting with literature review and dataset preparation, progressing to model distillation and optimization, and culminating in deployment, testing, and analysis. By enabling resource-efficient, real-time inference, this work aims to advance the development of responsive, autonomous mobile robots suitable for practical applications.
Goal: Investigate model distillation techniques and their application to world models for deployment on navigation tasks.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Elie Aljalbout (aljalbout AT ifi DOT uzh DOT ch) and Marco Cannici (cannici AT ifi DOT uzh DOT ch).
Thesis Type: Semester Project / Master Thesis
Advancing Space Navigation and Landing with Event-Based Camera in collaboration with the European Space Agency - Available
Description: Event-based cameras offer significant benefits in difficult robotic scenarios characterized by high-dynamic range and rapid motion. These are precisely the challenges faced by spacecraft during landings on celestial bodies like Mars or the Moon, where sudden light changes, fast dynamics relative to the surface, and the need for quick reaction times can overwhelm vision-based navigation systems relying on standard cameras. In this work, we aim to design novel spacecraft navigation methods for the descent and landing phases, exploiting the power efficiency and sparsity of event cameras. Particular effort will be dedicated to developing a lightweight frontend, utilizing asynchronous convolutional and graph neural networks to effectively harness the sparsity of event data, ensuring efficient and reliable processing during these critical phases. The project is in collaboration with European Space Agency at the European Space Research and Technology Centre (ESTEC) in Noordwijk (NL).
Goal: Investigate the use of asynchronous neural networks (either regular or spiking) for building an efficient frontend system capable of processing event-based data in real-time. Experiments will be conducted both pre-recorded dataset as well as on data collected during the project. We look for students with strong programming (Pyhton/Matlab) and computer vision backgrounds. Additionally, knowledge in machine learning frameworks (pytorch, tensorflow) is required.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Marco Cannici (cannici AT ifi DOT uzh DOT ch), Nikola Zubic (zubic AT ifi DOT uzh DOT ch), Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch].
Thesis Type: Master Thesis
Learning to Fly with Only Real-World Data - Available
Description: Reinforcement and imitation learning have already enabled great achievements in aerial robotics. However, most successes in this domain (as well as other domains in robotics) mostly rely on simulation as a tool for reducing the cost of training. This approach not only makes the assumption that a simulator can be provided for all tasks but also introduces the challenge of bridging the sim-to-real gap. The latter can be approached using multiple approaches such as state and action abstractions, domain randomization, and better system identification. However, it is not clear whether this gap can fully be fully bridged and whether the approaches we use to overcome the gap might introduce some disadvantages to the resulting system such as reduced performance, lack of robustness or simply overfitting behaviors. In this project, the goal is to learn agile flight policies using only real-world data and no access to a simulator. We will develop methods that can directly leverage real-world interactions for learning robust robot policies that do not overfit to the simulator. This comes with multiple challenges such as ensuring safe exploration and sample efficient training, but we already have some good ideas on approaching them.
Goal: Develop methods for learning to fly with real-world data and no simulator.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Elie Aljalbout (aljalbout AT ifi DOT uzh DOT ch), Angel Romero [roagui (at) ifi (dot) uzh (dot) ch], Ismail Geles [geles (at) ifi (dot) uzh (dot) ch].
Thesis Type: Semester Project / Master Thesis
Agile exploration of ballast tank with reinforcement learning - Available
Description: This project in concerned with the development of an agile drone system for autonomous exploration and inspection of ballast tanks using reinforcement learning (RL). Ballast tanks, essential for maintaining stability in marine vessels, pose significant challenges for inspection due to their confined, complex structures and GPS-denied environments. Traditional inspection methods, involving manual entry or remotely operated vehicles, are time-intensive, costly, and hazardous. Leveraging advancements in agile drone technology and RL, this project aims to design and implement a drone capable of navigating and inspecting these environments autonomously. The methodology involves creating a simulation environment replicating ballast tank conditions, training RL models for navigation and obstacle avoidance, and integrating these models into a hardware drone equipped with LIDAR, cameras, IMUs, and onboard processors. The trained system will be tested in controlled environments to evaluate performance in terms of navigation efficiency, area coverage, and robustness against uncertainties. Expected outcomes include a functional drone system that enhances inspection safety, efficiency, and cost-effectiveness, while providing a scalable framework for applying RL-driven drones to inspection in confined-space. The project can leverage our control and RL stacks for drone racing and augment them where necessary to enable the task of ballast tank exploration.
Goal: Develop methods for RL-based exploration of marine ballast tanks.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Elie Aljalbout (aljalbout AT ifi DOT uzh DOT ch), Angel Romero [roagui (at) ifi (dot) uzh (dot) ch], Ismail Geles [geles (at) ifi (dot) uzh (dot) ch].
Thesis Type: Semester Project / Master Thesis
Advancing Low-Latency Processing for Event-Based Neural Networks - Available
Description: Event cameras offer remarkable advantages, including ultra-high temporal resolution in the microsecond range, immunity to motion blur, and the ability to capture high-speed phenomena (https://youtu.be/AsRKQRWHbVs). These features make event cameras invaluable for applications like autonomous driving. However, efficiently processing the sparse event streams while maintaining low latency remains a difficult challenge. Previous research has focused on developing sparse update frameworks for event-based neural networks to reduce computational complexity, i.e., FLOPs. This project takes the next step by directly lowering the processing runtime to unlock the full potential of event cameras for real-time applications.
Goal: The focus of the project is to reduce runtime using common hardware (GPUs), which have been highly optimized for parallelization. The project will explore drastically new processing paradigms, which can potentially be transferred to standard frames. This ambitious project requires a strong sense of curiosity, self-motivation, and a principled approach to tackling research challenges. You should have solid Python programming skills and experience with at least one deep learning framework. If you’re excited about exploring cutting-edge techniques to push the boundaries, please feel free to contact us. **Key Requirement** - Background in Deep Learning: Proficiency in Python and familiarity with state-of-the-art deep learning frameworks. - Problem-Solving Skills: Ability to approach research problems in a principled way.
Contact Details: Nico Messikommer [nmessi (at) ifi (dot) uzh (dot) ch], Nikola Zubic [zubic (at) ifi (dot) uzh (dot) ch], Prof. Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch]
Thesis Type: Semester Project / Master Thesis
Gaussian Splatting meets Reinforcement Learning for Drone Racing - Available
Description: Gaussian Splatting (GS) is a compact, dense, and accurate map representation. Rendering a GS map is very fast. Thanks to these properties GS maps are appealing for localization. Our recent research has shown that training a Reinforcement Learning agent to learn how to control a racer drone from image pixels is possible. In this project, we will investigate the potential of building GS maps of racing tracks and use them to train an RL controller for drone racing. The goal is to achieve drone racing on any track. We will benchmark our solution against our current RL agent which relies on the detection of the racing gates.
Goal: The goal is to investigate the use of Gaussian splatting maps to train an RL control to fly racer drones. We look for students with strong programming skills (C++ and Python), computer vision (ideally have taken Prof. Scaramuzza's class), and robotic backgrounds.
Contact Details: Giovanni Cioffi [cioffi (at) ifi (dot) uzh (dot) ch], Jiaxu Xing [jixing (at) ifi (dot) uzh (dot) ch], Prof. Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch]
Thesis Type: Master Thesis
Event-based Reinforcement Learning Controller for Drone Racing - Available
Description: Event cameras are bio-inspired sensors that have many advantageous properties for robotic control. One of these properties is high temporal resolution. Events are generated in the order of microseconds. This makes event-based controllers appealing for fast-flying drones. Our recent research has shown that training a Reinforcement Learning agent to learn how to control a racer drone from image pixels is possible. In this project, we will develop an event-based simulator to train an RL controller for drone racing. The goal is to achieve the first drone racing flight with an event camera.
Goal: The goal is to investigate the use of an event camera to train an RL control to fly racer drones. We look for students with strong programming skills (C++ and Python), computer vision (ideally have taken Prof. Scaramuzza's class), and robotic backgrounds.
Contact Details: Giovanni Cioffi [cioffi (at) ifi (dot) uzh (dot) ch], Nico Messikommer [nmessi (at) ifi (dot) uzh (dot) ch], Prof. Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch]
Thesis Type: Master Thesis
Develop an RL environment for GoKart racing - Available
Description: The student will develop an RL training environment that is able to train an agent to race in a race track. This environment will support different RL algorithm (PPO, SAC, etc). The student will first start with building the environment itself, including the track and a potential ‘car’ with its basic dynamics. After this, the student will develop a reward function that is able to take the car to its limits of handling, similar to what we have in the drones.
Goal: At the end of the project, the created environment will be able to train a car agent that is able to race time-optimally through a track
Contact Details: The project will be supervised by both Professor Emilio Frazzoli and Professor Davide Scaramuzza's groups. The experiments will take place at the Winterthur testing ground, utilizing our fleet of autonomous racing karts. Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Angel Romero (roagui AT ifi DOT uzh DOT ch), Leonard Bauersfeld (bauersfeld AT ifi DOT uzh DOT ch), Ismail Geles (geles AT ifi DOT uzh DOT ch), Jiaxu Xing (jixing AT ifi DOT uzh DOT ch) and Maurilio di Cicco (mdicicco AT ethz DOT ch)
Thesis Type: Semester Project / Master Thesis
Fine-tuning Policies in the Real World with Reinforcement Learning - Available
Description: Training sub-optimal policies is relatively straightforward and provides a solid foundation for reinforcement learning (RL) agents. However, these policies cannot improve online in the real world, such as when racing drones with RL. Current methods fall short in enabling drones to adapt and optimize their performance during deployment. Imagine a drone equipped with an initial sub-optimal policy that can navigate a race course but not with maximum efficiency. As the drone races, it learns to optimize its maneuvers in real-time, becoming faster and more agile with each lap.
Goal: This project aims to explore online fine-tuning in the real world of sub-optimal policies using RL, allowing racing drones to improve continuously through real-world interactions.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Ismail Geles [geles (at) ifi (dot) uzh (dot) ch], Elie Aljalbout [aljalbout (at) ifi (dot) uzh (dot) ch]
Thesis Type: Semester Project / Master Thesis
Inverse Reinforcement Learning from Expert Pilots - Available
Description: Drone racing demands split-second decisions and precise maneuvers. However, training drones for such races relies heavily on crafted reward functions. These methods require significant human effort in design choices and limit the flexibility of learned behaviors. Inverse Reinforcement Learning (IRL) offers a promising alternative. IRL allows an AI agent to learn a reward function by observing expert demonstrations. Imagine an AI agent analyzing recordings of champion drone pilots navigating challenging race courses. Through IRL, the agent can infer the implicit factors that contribute to success in drone racing, such as speed and agility.
Goal: We want to explore the application of Inverse Reinforcement Learning (IRL) for training RL agents performing drone races or FPV freestyle to develop methods that extract valuable knowledge from the actions and implicit understanding of expert pilots. This knowledge will then be translated into a robust reward function suitable for autonomous drone flights.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to: Ismail Geles [geles (at) ifi (dot) uzh (dot) ch], Elie Aljalbout [aljalbout (at) ifi (dot) uzh (dot) ch], Angel Romero [roagui (at) ifi (dot) uzh (dot) ch]
Thesis Type: Semester Project / Master Thesis
Language-guided Drone Control - Available
Description: Imagine controlling a drone with simple, natural language instructions like "fly through the gap" or "follow that red car” – this is the vision behind language-guided drone control. However, translating natural language instructions into precise drone maneuvers presents a unique challenge. Drones operate in a dynamic environment, requiring real-time interpretation of user intent and the ability to adapt to unforeseen obstacles.
Goal: This project focuses on developing a novel system for language-guided drone control using recent advances in Vision Language Models (VLMs). Our goal is to bridge the gap between human language and drone actions. We aim to create a system that can understand natural language instructions, translate them into safe and efficient flight instructions, and control the drone accordingly, making it accessible to a wider range of users and enabling more intuitive human-drone interaction.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to: Ismail Geles [geles (at) ifi (dot) uzh (dot) ch], Elie Aljalbout [aljalbout (at) ifi (dot) uzh (dot) ch]
Thesis Type: Semester Project / Master Thesis
Vision-based Navigation in Dynamic Environment via Reinforcement Learning - Available
Description: In this project, the goal is to develop a vision-based policy that enables autonomous navigation in complex, cluttered environments. The learned policy should enable the robot to effectively reach a designated target based on visual input while safely avoiding encountered obstacles. Some of the use cases for this approach will be to ensure a safe landing on a moving target in a cluttered environment or to track a moving target in the wild. Applicants should have a solid understanding of reinforcement learning, machine learning experience (PyTorch), and programming experience in C++ and Python.
Goal: Develop such a policy based on an existing reinforcement learning pipeline. Extend the training environment adapted for the task definition. The approach will be demonstrated and validated both in simulated and real-world settings.
Contact Details: Jiaxu Xing (jixing@ifi.uzh.ch), Leonard Bauersfeld (bauersfeld@ifi.uzh.ch)
Thesis Type: Master Thesis
Learning Rapid UAV Exploration with Foundation Models - Available
Description: In this project, our objective is to efficiently explore unknown indoor environments using UAVs. Recent research has demonstrated significant success in integrating foundational models with robotic systems. Leveraging these foundational models, the drone will employ learned semantic relationships from large-world-scale data to actively explore and navigate through unknown environments. While most prior research has focused on ground-based robots, this project aims to investigate the potential of integrating foundational models with aerial robots to introduce more agility and flexibility. Applicants should have a solid understanding of mobile robot navigation, machine learning experience (PyTorch), and programming experience in C++ and Python.
Goal: Develop such a framework in simulation and conduct a comprehensive evaluation and analysis. If feasible, deploy such a model in a real-world environment.
Contact Details: Jiaxu Xing (jixing@ifi.uzh.ch), Nico Messikommer (nmessi@ifi.uzh.ch)
Thesis Type: Semester Project / Master Thesis
Foundation models for vision-based reinforcement learning - Available
Description: Vision-based reinforcement learning (RL) is more sample inefficient and more complex to train compared to state-based RL because the policy is learned directly from raw image pixels rather than from the robot state. In comparison to state-based RL, vision-based policies need to learn some form of visual perception or image understanding from scratch, which makes them way more complex to learn and to generalise. Foundation models trained on vast datasets have shown promising potential in outputting feature representations that are useful for a large variety of downstream tasks. In this project, we investigate the capabilities of such models to provide robust feature representations for learning control policies. We plan to study how different feature representations affect the exploration behavior of RL policies, the resulting sample complexity and the generalisation and robustness to out-of-distribution samples. This will include training different RL policies on various robotics tasks using various intermediate feature representations.
Goal: Study the effect of feature representations from different foundation models on learning robotic control tasks with deep RL and imitation learning.
Contact Details: Elie Aljalbout [aljalbout (AT) ifi (DOT) uzh (DOT) ch], Jiaxu Xing [jixing (AT) ifi (DOT) uzh (DOT) ch], Ismail Geles [geles (AT) ifi (DOT) uzh (DOT) ch]
Thesis Type: Semester Project / Master Thesis
Offline-to-Online (model-based) Reinforcement Learning Transfer and Finetuning for Vision-based Robot Control - Available
Description: Vision-based reinforcement learning (RL) is often sample-inefficient and computationally very expensive. One way to bootstrap the learning process is to leverage offline interaction data. However, this approach faces significant challenges, including out-of-distribution (OOD) generalization and neural network plasticity. The goal of this project is to explore methods for transferring offline policies to the online regime in a way that alleviates the OOD problem. By initially training the robot's policies system offline, the project seeks to leverage the knowledge of existing robot interaction data to bootstrap the learning of new policies. The focus is on overcoming domain shift problems and exploring innovative ways to fine-tune the model and policy using online interactions, effectively bridging the gap between offline and online learning. This advancement would enable us to efficiently leverage offline data (e.g. from human or expert agent demonstrations or previous experiments) for training vision-based robotic policies. This would/could involve (but is not limited to) developing methods for uncertainty estimation and handling, domain adaptation for model-based RL, pessimism (during offline training), and curiosity (during finetuning) in RL methods.
Goal: Develop methods for transferring control policies learned offline to the online inference/finetuning regime.
Contact Details: Elie Aljalbout [aljalbout (AT) ifi (DOT) uzh (DOT) ch]
Thesis Type: Master Thesis
Incorporating expert data into model-based reinforcement learning - Available
Description: Model-based reinforcement learning (MBRL) methods have greatly improved sample efficiency compared to model-free approaches. Nonetheless, the amount of samples and compute required to train these methods remains too large for real-world training of robot control policies. Ideally, we should be able to leverage expert data (collected by human or artificial agents) to bootstrap MBRL. The exact way to leverage such data is yet unclear and many options are available. For instance, it is possible to only use such data for training high-accuracy dynamics models (world models) that are useful for multiple tasks. Alternatively, expert data can (also) be used for training the policy. Additionally, pretraining MBRL components can itself be very challenging as offline expert data is typically sampled from a very narrow distribution of behaviors, which makes finetuning non-trivial in out-of-distributions areas of the robot’s state-action space. In this thesis, you will look at different ways of incorporating expert data in MBRL and ideally propose new approaches to best do that. You will test these methods in both simulation (simulated drone, wheeled, legged) and in the real world on our quadrotor platform. You will gain insights into MBRL, sim-to-real transfer, robot control. Requirements: Applicants should have a strong background in machine learning, computer vision, and proficiency in Python programming. Familiarity with deep learning frameworks such as PyTorch is desirable.
Goal: Study and propose methods for leveraging expert data in model-based reinforcement learning for quadrotor flight control.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Elie Aljalbout [aljalbout (at) ifi (dot) uzh (dot) ch], and Jiaxu Xing [jixing (at) ifi (dot) uzh (dot) ch]
Thesis Type: Semester Project / Master Thesis
Model-based Reinforcement Learning and World Models for Autonomous Drone Racing - Available
Goal: The objective of this project is to implement state of the art model-based RL and world models for drones to a model-based Reinforcement Learning pipeline. The goal is to investigate potential performance improvements of the reinforcement learning algorithm by incorporating a model of the drone's dynamics, which will allow the algorithm to make more informed decisions. This will result in faster learning and better generalization, leading to better performance in real-world scenarios. To accomplish this goal, the student will need to research and implement various model-based reinforcement learning algorithms and evaluate their performance in a simulation environment for drone navigation. The student will also need to fine-tune the parameters of the algorithm to achieve optimal performance. The final product will be a pipeline that can be used to train a drone to navigate in a variety of environments with improved efficiency and accuracy. Applicants should have a strong background in both model-free and model-based reinforcement learning techniques, programming in C++ and Python, and a good understanding of nonlinear dynamic systems. Additional experience in signal processing, machine learning, as well as being comfortable operating in a hands-on environment is highly desired.
Contact Details: Please send your CV and transcripts (bachelor and master), and any projects you have worked on that you find interesting to Angel Romero (roagui AT ifi DOT uzh DOT ch), Ismail Geles (geles AT ifi DOT uzh DOT ch) and Elie Aljalbout (aljalbout AT ifi DOT uzh DOT ch)
Thesis Type: Semester Project / Master Thesis
Energy-Efficient Path Planning for Autonomous Quadrotors in Inspection Tasks - Available
Description: Autonomous quadrotors are increasingly used in inspection tasks, where flight time is often limited by battery capacity. In these operations, reducing energy consumption is essential, especially when quadrotors must navigate complex paths near inspection targets. Traditional path planning methods often overlook energy costs, which limits their effectiveness in real-world applications. This project aims to explore and evaluate state-of-the-art path planning approaches that incorporate energy efficiency into trajectory optimization. Various planning techniques will be tested to identify the most suitable methods for minimizing energy consumption, ensuring smooth navigation, and maximizing inspection coverage within a single battery charge. Strong programming skills in Python/C++ and a background in robotics or autonomous systems are required. Experience in motion planning, machine learning, or energy modeling is beneficial but not essential.
Goal: The goal of this project is to develop, implement, and test an energy-efficient waypoint path planning method that improves quadrotor endurance in inspection tasks, maximizing inspection coverage within a single battery cycle.
Contact Details: Leonard Bauersfeld (bauersfeld AT ifi DOT uzh DOT ch), Elie Aljalbout (aljalbout AT ifi DOT uzh DOT ch)
Thesis Type: Semester Project / Master Thesis
Generating Realistic Event Camera Data with Generative AI - Available
Description: Event cameras offer remarkable advantages, including ultra-high temporal resolution in the microsecond range, immunity to motion blur, and the ability to capture high-speed phenomena (https://youtu.be/AsRKQRWHbVs). These features make event cameras invaluable for applications like autonomous driving. However, the limited availability of event-based datasets poses a challenge for research and industry. This project aims to address this gap by generating synthetic event data from standard images. The main challenge lies in making these synthetic events realistically approximate the noise patterns and characteristics of real event cameras. Leveraging advanced deep learning techniques, the project focuses on developing high-quality synthetic event data that closely approximates real-world events, providing a useful tool for event-based vision.
Goal: In this project, you will work with cutting-edge deep learning models to create synthetic event data from conventional images, focusing on bridging the gap between simulated and real event-based data. You will have the opportunity to explore advanced generative AI techniques and leverage state-of-the-art foundation models to produce highly realistic events. Along the way, you will gain a thorough understanding of how event cameras operate and their unique capabilities compared to traditional cameras. A strong background in deep learning is essential for this project, as you will be diving into complex methods at the forefront of AI research. **Key Requirements** - Background in Deep Learning: Experience working with state-of-the-art deep learning frameworks. - Programming Proficiency: Solid Python programming skills.
Contact Details: Nico Messikommer [nmessi (at) ifi (dot) uzh (dot) ch], Nikola Zubic [zubic (at) ifi (dot) uzh (dot) ch], Prof. Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch]
Thesis Type: Semester Project / Master Thesis
Enhancing Robotic Motor Policies with Event Cameras - Available
Description: Motor policies trained in simulations have shown remarkable real-world performance in robotics. This project aims to further enhance their robustness by incorporating data from event cameras. Event cameras offer remarkable advantages, including ultra-high temporal resolution in the microsecond range, immunity to motion blur, and the ability to capture high-speed phenomena (https://youtu.be/AsRKQRWHbVs). These features make event cameras ideal for improving robotic performance in challenging conditions such as low light or fast motion. A major challenge lies in the inefficiency of current simulation methods, which rely on rendering numerous high-frame-rate images to generate synthetic event data. This project addresses the issue by developing a shared embedding space for event and frame-based data, enabling motor policies to be trained with simulated frames and seamlessly deployed using real-world event data.
Goal: In this project, you will explore Unsupervised Domain Adaptation (UDA) techniques to transfer motor policies from frame-based to event-based data, building on a prior student work published at ECCV22. You will test the proposed approach in simulation environments, with the potential for real-world experiments in our drone arena. A strong focus will be placed on demonstrating the advantages of event cameras in demanding scenarios like fast motion and low-light conditions. This project requires a solid background in deep learning, as you will employ advanced techniques for task transfer between data modalities. If you’re excited about working on cutting-edge robotics and AI methods, we’d be happy to provide more details! **Key Requirements** - Background in Deep Learning and/or Reinforcement Learning: Experience working with state-of-the-art deep learning frameworks. - Interest in Robotics: A strong motivation to apply developed methods in the real-world.
Contact Details: Nico Messikommer [nmessi (at) ifi (dot) uzh (dot) ch], Jiaxu Xing [jixing (at) ifi (dot) uzh (dot) ch], Prof. Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch]
Thesis Type: Semester Project / Master Thesis
From Floorplan to Flight - Available
Description: Drone racing is considered a proxy task for many real-world applications, including search and rescue missions. In such an application, doorframes, corridors, and other features of the environment could be used to as “gates” the drone needs to pass through. Relevant information on the layout could be extracted from a floor plan of the environment in which the drone is tasked to operate autonomously. To be able to train such navigation policies, the first step is to simulate the environment.
Goal: This project aims to develop a simulation of environments that procedurally generate corridors and doors based on an input floor plan. We will compare model-based approaches (placing objects according to some heuristic/rules) with learning-based approaches, which directly generate the model based on the floorplan. Requirements: - Machine learning experience (PyTorch) - Excellent programming skills in C++ and Python - 3D Modeling experience (CAD, Blender) is a plus
Contact Details: Leonard Bauersfeld (bauersfeld@ifi.uzh.ch), Marco Cannici (cannici@ifi.uzh.ch)
Thesis Type: Master Thesis
Vision-based End-to-End Flight with Obstacle Avoidance - Available
Description: Recent progress in drone racing enables end-to-end vision-based drone racing, directly from images to control commands _without explicit state estimation_. In this project, we address the challenge of unforeseen obstacles and changes to the racing environment. The goal is to develop a control policy that can race through a predefined track but is robust to minor track layout changes and gate placement changes. Additionally, the policy should avoid obstacles that are placed on the racetrack, mimicking real-world applications where unforeseen obstacles can be present at any time. Requirements: - Machine learning experience (PyTorch) - Excellent programming skills in C++ and Python
Contact Details: Leonard Bauersfeld (bauersfeld@ifi.uzh.ch), Ismail Geles (geles@ifi.uzh.ch)
Thesis Type: Master Thesis
Event-based Particle Image Velocimetry - Available
Description: When drones are operated in industrial environments, they are often flown in close proximity to large structures, such as bridges, buildings or ballast tanks. In those applications, the interactions of the induced flow produced by the drone’s propellers with the surrounding structures are significant and pose challenges to the stability and control of the vehicle. A common methodology to measure the airflow is particle image velocimetry (PIV). Here, smoke and small particles suspended in the surrounding air are tracked to estimate the flow field. In this project, we aim to leverage the high temporal resolution of event cameras to perform smoke-PIV, overcoming the main limitation of frame-based cameras in PIV setups. Applicants should have a strong background in machine learning and programming with Python/C++. Experience in fluid mechanics is beneficial but not a hard requirement.
Goal: The goal of the project is to develop and successfully demonstrate a PIV method in the real world.
Contact Details: Leonard Bauersfeld (bauersfeld@ifi.uzh.ch), Koen Muller (kmuller@ethz.ch)
Thesis Type: Semester Project / Master Thesis
Learning Robust Agile Flight via Adaptive Curriculum - Available
Description: Reinforcement learning-based controllers have demonstrated remarkable success in enabling fast and agile flight. Currently, the training process of these reinforcement learning controllers relies on a static, pre-defined curriculum. In this project, our objective is to develop a dynamic and adaptable curriculum to enhance the robustness of the learning-based controllers. This curriculum will continually adapt in an online fashion based on the controller's performance during the training process. By using the adaptive curriculum, we expect the reinforcement learning controllers to enable more diverse, generalizable, and robust performance in unforeseen scenarios. Applicants should have a solid understanding of reinforcement learning, machine learning experience (PyTorch), and programming experience in C++ and Python.
Goal: Improve the robustness and generalizability of the training framework and validate the method in different navigation task settings. The approach will be demonstrated and validated both in simulated and real-world settings.
Contact Details: Jiaxu Xing (jixing@ifi.uzh.ch), Nico Messikommer (nmessi@ifi.uzh.ch)
Thesis Type: Semester Project / Master Thesis