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, ...).
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. Applicants are expected to be proficient in Python, C++, and Git.
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. Applicants are expected to be proficient in Python, C++, and Git.
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], Leonard Bauersfeld [bauersfeld (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
Agile Flight of Flexible Drones in Confined Spaces - Available
Description: This master's project (or thesis) examines the application of reinforcement learning and numerical optimal control to achieve high-performance, agile flight of a flexible quadrotor in confined environments. While high-fidelity models exist for many robotic platforms, their computational demands often limit their use in real-time control scenarios. This project aims to identify and utilize a model of a flexible quadrotor that strikes a balance between accuracy and efficiency. The approach combines model predictive control with precise numerical integration over a short initial horizon and a simplified, lower-fidelity model for longer-term planning. This hybrid strategy enables long prediction horizons, which are crucial for executing agile maneuvers, such as flying through narrow gaps or navigating tight indoor spaces. A reinforcement learning policy will be developed using the lab’s high-performance simulators to complement and enhance the control strategy. The project offers the opportunity to work at the intersection of learning, planning, and control, with a strong emphasis on deploying high-speed, intelligent robotics in challenging real-world scenarios. It suits students interested in advanced control, dynamics, reinforcement learning, and robotics. Applicants should have proficiency in model predictive control, numerical optimization, and reinforcement learning, as well as experience in programming with Python and C++. Initial exposure to NMPC solvers such as acados is expected, as well as familiarity with simulation software and real-time data processing. A solid understanding of drone dynamics and control systems, combined with a background in signal processing and nonlinear dynamic systems.
Goal: This project can be taken as a student project or a master's thesis. Student projects can focus on reinforcement learning or model predictive control, while master's theses are required to compare both.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Rudolf Reiter (rreiter AT ifi DOT uzh DOT ch), Angel Romero (roagui AT ifi DOT uzh DOT ch), and Leonard Bauersfeld (bauersfeld AT ifi DOT uzh DOT ch).
Thesis Type: Semester Project / Master Thesis
Vision-Based World Models for Real-Time Robot Control - Available
Description: This master's project focuses on enabling real-time, vision-based control for quadrotors by distilling large, complex world models into lightweight versions suitable for deployment on resource-constrained platforms. The goal is to achieve fast, efficient inference from camera inputs, supporting agile indoor navigation in previously unseen environments. Large-scale vision models capable of generating and understanding complex scenes are typically too computationally intensive for onboard use. This project addresses that challenge by applying model distillation techniques to transfer knowledge from a pre-trained, high-capacity model to a smaller, faster one. The distilled models will be deployed on quadrotors to evaluate real-world performance, focusing on latency, energy consumption, and navigation success. Beyond standard RGB input, the project will also investigate using additional visual modalities like depth and semantic segmentation to enhance control capabilities. The work will follow a structured timeline, starting with a literature review and dataset setup, moving through distillation and model optimization, and ending with deployment and testing. This project is an excellent fit for students interested in robotics, computer vision, and efficient deep learning, and it offers the chance to contribute to the future of responsive, autonomous robotic systems. **Applicant Requirements:** - Proficiency in reinforcement learning, robotics, and computer vision - Strong programming skills with Python - First experience with large neural network world models - Knowledge of simulation software and real-time data processing - Understanding of drone dynamics and control systems
Goal: Investigate model distillation techniques and their application to vision-based 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 Rudolf Reiter (rreiter AT ifi DOT uzh DOT ch) and Daniel Zhai (dzhai (at) ifi (dot) uzh (dot) ch).
Thesis Type: Semester Project / Master Thesis
Vision-Based Reinforcement Learning in the Real World - Available
Description: This master's project offers an exciting opportunity to work on real-world vision-based drone flight without relying on simulators. The goal is to develop learning algorithms that enable quadrotors to fly autonomously using visual input, learned directly from real-world experience. By avoiding simulation, this approach opens up new possibilities for the future of robotics. A significant focus of the project is achieving high sample efficiency and designing a robust safety framework that enables effective exploration by leveraging the latest research results. The project will begin with state-based learning as an intermediate step, progressing toward complete vision-based learning. It builds on recent research advances and a well-established drone navigation and control software stack. The lab provides access to multiple vision-capable quadrotors ready for immediate use. This project is ideal for motivated master’s students interested in robotics, learning systems, and real-world deployment. It offers a rare chance to contribute to a high-impact area at the intersection of machine learning, control, and computer vision, with strong potential for further academic or industrial opportunities. Applicants should have proficiency in computer vision, reinforcement learning, and robotics, as well as strong programming skills in Python and C++. Initial experience with large neural network world models is expected, as well as familiarity with simulation software and real-time data processing. A solid understanding of drone dynamics and control systems is also essential.
Goal: The goal is to investigate how the latest reinforcement learning advances push the limits of learning real-world tasks such as agile vision-based flight.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Rudolf Reiter (rreiter AT ifi DOT uzh DOT ch) and Angel Romero (roagui AT ifi DOT uzh DOT ch)
Thesis Type: Semester Project / Master Thesis
Meta-model-based-RL for adaptive flight control - Available
Description: Drone dynamics can change significantly during flight due to variations in load, battery levels, and environmental factors such as wind conditions. These dynamic changes can adversely affect the drone's performance and stability, making it crucial to develop adaptive control strategies. This research project aims to develop and evaluate a meta model-based reinforcement learning (RL) framework to address these variable dynamics. By integrating dynamic models that account for these variations and employing meta-learning techniques, the proposed method seeks to enhance the adaptability and performance of drones in dynamic environments. The project will involve learning dynamic models for the drone, implementing a meta model-based RL framework, and evaluating its performance in both simulated and real-world scenarios, aiming for improved stability, efficiency, and task performance compared to existing RL approaches and traditional control methods. Successful completion of this project will contribute to the advancement of autonomous drone technology, offering robust and efficient solutions for various applications. Applicants are expected to be proficient in Python, C++, and Git.
Goal: Develop methods for meta (model-based) RL to handle variable drone dynamics.
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: 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. Applicants are expected to be proficient in Python, C++, and Git.
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
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) to: Roberto Pellerito (rpellerito@ifi.uzh.ch), Marco Cannici (cannici@ifi.uzh.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
Thesis Type: Master Thesis
Time-continuous Facial Motion Capture Using Event Cameras - Available
Description: Traditional facial motion capture systems often rely on marker-based methods or multi-camera rigs to track facial movements. However, these approaches can be limited in capturing fine details such as subtle wrinkles and micro-expressions. Recent advancements in learning-based techniques have enabled high-fidelity facial tracking using monocular RGB images, but the temporal resolution is constrained by the frame rate of conventional cameras. Event-based cameras offer a promising alternative, providing superior temporal resolution without the need for costly and bulky high-speed RGB cameras. This project aims to leverage the advantages of event-based cameras to achieve unprecedented quality in tracking subtle facial movements.
Goal: Develop a facial motion capture system that utilizes event-based cameras to accurately track fine facial movements, including micro-expressions and subtle wrinkles. The system should overcome the limitations of traditional methods by providing higher temporal resolution and capturing intricate facial details.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master) to: Roberto Pellerito (rpellerito@ifi.uzh.ch), Nico Messikommer (nmessi@ifi.uzh.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
Thesis Type: Semester Project / Bachelor Thesis / Master Thesis
Better Scaling Laws for Neuromorphic Systems - Available
Description: This project explores and extends the novel "deep state-space models" framework by leveraging their transfer function representations. In contrast to time-domain parameterizations (e.g., S4 layers), transfer function parameterization enables direct computation of the model’s corresponding convolutional kernel via a single Fast Fourier Transform. This is state-free, and in theory, it would maintain constant memory and computational overhead regardless of the state size, therefore offering substantial speed and scalability advantages over existing approaches. Building on these promising theoretical results, this project aims to derive better scaling laws for neuromorphic systems by studying and deploying state-free inference in diverse long-sequence and event-based vision applications.
Goal: Implement the transfer function-based state-space model, then comprehensively benchmark its training speed, memory usage, and performance on neuromorphic and event-based vision tasks. Investigate how state-free inference behaves as model size and sequence length grow, deriving empirical or theoretical scaling relationships. Compare this approach with other state-of-the-art methods (e.g., S4, Transformer-based models) in terms of speed, memory footprint, and model accuracy or task performance. Prerequisites include familiarity with basics of LTI systems and linear ODEs, and Python programming language.
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
Electrical Flow-Based Graph Embeddings for Event-based Vision and other downstream tasks - Available
Description: Besides RPG, this project will be co-supervised by Aurelio Sulser (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), Aurelio Sulser (asulser@student.ethz.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
Thesis Type: 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
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
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
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
Automatic Failure Detection for Drones - Available
Description: Automatic failure detection is an essential topic for aerial robots as small failures can already lead to catastrophic crashes. Classical methods in fault detection typically use a system model as a reference and check that the observed system dynamics are within a certain error margin. In this project, we want to explore sequence modeling as an alternative approach that feeds all available sensor data into a neural network. The network will be pre-trained on simulation data and finetuned on real-world flight data. Such a machine learning-based approach has significant potential because neural networks are very good at picking up patterns in the data that are hidden/invisible to hand-crafted detection algorithms.
Goal: The goal of the project is to develop a method that is able to automatically detect the health-status of a drone from minimal flight data, such as taking off or performing a short 'check' maneuver.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Leonard Bauersfeld [bauersfeld (at) ifi (dot) uzh (dot) ch], Ismail Geles [geles (at) ifi (dot) uzh (dot) ch], and Davide Scaramuzza (sdavide (at) ifi (dot) uzh (dot) ch).
Thesis Type: Semester Project / Master Thesis
Vision-based Tracking of Falling Objects - Available
Description: Drones are highly agile and thus ideally suited to track falling objects over longer distances. In this project, we want to explore vision-based tracking of slowly falling objects such as leaves or snowflakes. The drone should detect the object in the view of the onboard camera and issue control commands such that the object remains in the center of the field of view. The problem is challenging from a control point of view, as a drone can not accelerate downwards and thus has minimal control authority. At the same time, the perception pipeline must cope with tracking an object that can arbitrarily rotate during the fall.
Goal: The goal is to build and deploy the vision-based tracking system for slowly falling objects both in simulation and the real world. Such a method could, for example, be used to track falling snow over longer distances and characterize the evolution of a single snow-flake over time. Interested students should have experience in both Python and C++. Furthermore, they should have taken courses both in computer vision (e.g. Vision Algorithms for Mobile Robots) as well as introductory courses on control systems.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Leonard Bauersfeld [bauersfeld (at) ifi (dot) uzh (dot) ch], Nico Messikommer [nmessi (at) ifi (dot) uzh (dot) ch], and Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) 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 knowledge in machine learning and programming experience with Python and C++. Experience in fluid mechanics is beneficial but not a 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 Agile Flight with Nano-drones - Available
Description: Autonomous nano-drones, i.e., as big as the palm of your hand, are increasingly getting attention: their tiny form factor can be a game-changer in many applications that are out of reach for larger drones, for example inspection of collapsed buildings, or assistance in natural disaster areas. To operate effectively in such time-sensitive situations, these tiny drones must achieve agile flight capabilities. While micro-drones (approximately 50 cm in diameter) have already demonstrated impressive agility, nano-drones still lag behind. This project aims to improve the agility of nano-drones by developing a deep learning–based approach for high-speed obstacle avoidance using only onboard resources. **Prerequisites** Proficiency in Python and C programming. Background in Deep Learning. Experience in programming MicroControllers.
Goal: The goal is to enable nano-drones to navigate cluttered environments at high speeds. We will leverage Reinforcement Learning in combination with imitation learning from a privileged expert within a simulated environment. The trained policy will then be deployed and evaluated in-field on a Crazyflie brushless nano-drone, leveraging a custom PCB with an integrated camera and microcontroller, developed at the Integrated Systems Laboratory (IIS), ETH Zurich.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Lorenzo Lamberti [llamberti (at) iis (dot) ee (dot) ethz (dot) ch], Nico Messikommer [nmessi (at) ifi (dot) uzh (dot) ch], Daniele Palossi [daniele (dot) palossi (at) iis (dot) ee (dot) ethz (dot) ch], Luca Benini [lbenini (at) iis (dot) ee (dot) ethz (dot) ch], and Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch].
Thesis Type: Master Thesis
Ultrasonic and Visual Sensor Fusion on Nano-drones - Available
Description: Autonomous nano-sized drones, with palm-sized form factor, are particularly well-suited for exploration in confined and cluttered environments. A pivotal requirement for exploration is visual-based perception and navigation. However, vision-based systems can fail in challenging conditions such as darkness, extreme brightness, fog, dust, or when facing transparent materials. In contrast, ultrasonic sensors provide reliable collision detection in these scenarios, making them a valuable complementary sensing modality. This project aims to develop a robust deep learning–based navigation system that fuses data from an ultrasonic sensor and a traditional frame-based camera to enhance obstacle avoidance capabilities. **Prerequisites** Proficiency in Python and C programming. Background in Deep Learning. Experience in programming MicroControllers.
Goal: Develop a deep neural network (DNN) for obstacle avoidance on nano-drones by fusing traditional camera frames with ultrasonic sensor data. Optimization of the algorithm for the execution on resource-constrained MicroController Units. Collect and process real-world and/or simulated data to train and evaluate the model. Validate the system through in-field experiments, assessing the obstacle avoidance capabilities of a nano-drone.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Lorenzo Lamberti [llamberti (at) iis (dot) ee (dot) ethz (dot) ch], Nico Messikommer [nmessi (at) ifi (dot) uzh (dot) ch], Daniele Palossi [daniele (dot) palossi (at) iis (dot) ee (dot) ethz (dot) ch], Luca Benini [lbenini (at) iis (dot) ee (dot) ethz (dot) ch], and Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch].
Thesis Type: Master Thesis
Events and Frames Fusion for Visual Odometry on Nano-sized Drones - Available
Description: Autonomous nano-sized drones hold great potential for robotic applications, such as inspection in confined and cluttered environments. However, their small form factor imposes strict limitations on onboard computational power, memory, and sensor capabilities, posing significant challenges in achieving autonomous functionalities, such as robust and accurate state estimation. State-of-the-art (SoA) Visual Odometry (VO) algorithms leverage the fusion of traditional frame-based camera images with event-based data streams to achieve robust motion estimation. However, existing SoA VO models are still too compute/memory intensive to be integrated on the low-power processors of nano-drones. This thesis aims to optimize SoA deep learning-based VO algorithms and enable efficient execution on MicroController Units. **Prerequisites** Proficiency in Python and C programming. Background in Deep Learning. Experience in programming MicroControllers.
Goal: This project aims to develop a deep neural network (DNN) for Visual Odometry that fuses traditional frame-based images with event camera data streams. The focus will be on optimizing the SoA model to reduce its computational complexity and memory footprint. The algorithm will be validated on real and/or simulated datasets to assess its robustness, and deployed on a microcontroller to profile its execution latency.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Lorenzo Lamberti [llamberti (at) iis (dot) ee (dot) ethz (dot) ch], Nico Messikommer [nmessi (at) ifi (dot) uzh (dot) ch], Daniele Palossi [daniele (dot) palossi (at) iis (dot) ee (dot) ethz (dot) ch], Luca Benini [lbenini (at) iis (dot) ee (dot) ethz (dot) ch], and Davide Scaramuzza [sdavide (at) ifi (dot) uzh (dot) ch].
Thesis Type: Master Thesis