Student Projects

We aim to learn vision-based policies in the real world using state-of-the-art model-based reinforcement learning.

This project aims to use vision-based world models as a basis for model-based reinforcement learning, aiming to achieve a generalizable approach for drone navigation.

The project aims to create a controller for an interesting and challenging type of quadrotor, where the rotors are connected via flexible joints.

Design and implement efficient event-based networks to achieve low latency inference.

Use Inverse Reinforcement Learning (IRL) to learn reward functions from previous expert drone demonstrations.

Explore online fine-tuning in the real world of sub-optimal policies.

Develop a vision-based aerial transportation system with reinforcement / imitation learning.

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.

Autonomous quadrotors are increasingly used in inspection tasks, where flight time is often limited by battery capacity. his project aims to explore and evaluate state-of-the-art path planning approaches that incorporate energy efficiency into trajectory optimization.

Recent research has demonstrated significant success in integrating foundational models with robotic systems. In this project, we aim to investigate how these foundational models can enhance the vision-based navigation of UAVs. The drone will utilize learned semantic relationships from extensive world-scale data to actively explore and navigate through unfamiliar environments. While previous research primarily focused on ground-based robots, our project seeks to explore the potential of integrating foundational models with aerial robots to enhance agility and flexibility.

In this project, we are going to develop a vision-based reinforcement learning policy for drone navigation in dynamic environments. The policy should adapt to two potentially conflicting navigation objectives: maximizing the visibility of a visual object as a perceptual constraint and obstacle avoidance to ensure safe flight.

This project focuses on developing robust reinforcement learning controllers for agile drone navigation using adaptive curricula. Commonly, these controllers are trained with a static, pre-defined curriculum. The goal is to develop a dynamic, adaptive curriculum that evolves online based on the agents' performance to increase the robustness of the controllers.

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.

Perform knowledge distillation from Transformers to more energy-efficient neural network architectures for Event-based Vision.

Study the application of Long Sequence Modeling techniques within Reinforcement Learning (RL) to improve autonomous drone racing capabilities.

In this project, you will investigate the use of event-based cameras for vision-based landing on celestial bodies such as Mars or the Moon.

This project explores and extends the novel "deep state-space models" framework by leveraging their transfer function representations.

This project develops event-based representation learning methods for control tasks in environments with visual distractors, leveraging sparse, high-temporal-resolution event data to improve robustness and efficiency over traditional frame-based approaches.

This thesis develops hardware-optimized recurrent neural network architectures with novel parallelization and kernel-level strategies to efficiently process event-based vision data for real-time neuromorphic and GPU-based applications.

This project explores integrating event-based vision into LLMs through custom layers and knowledge distillation, enabling efficient processing of sparse, asynchronous data for dynamic scene understanding in real-world applications.

This thesis explores novel spiking neural network architectures that leverage event-based vision data and emergent neural synchronization to enable efficient and robust temporal reasoning for dynamic scene understanding and sequential tasks.