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, ...).
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
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
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
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), Rudolf Reiter (rreiter AT ifi DOT uzh DOT ch)
Thesis Type: Semester Project / 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
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. Applicants are expected to be proficient in Python, C++, and Git.
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: 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. 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
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