Robotics and Perception

Open Opportunities

Learning Robust Agile Flight via Adaptive Curriculum University of Zurich Robotics and Perception 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. Engineering and Technology Master Thesis, Semester Project

Improving SLAM in the Operating Room Using Event Cameras University of Zurich Robotics and Perception Other organizations: Research in Orthopedic Computer Science This project focuses on enhancing SLAM (Simultaneous Localization and Mapping) in operating rooms using event cameras, which outperform traditional cameras in dynamic range, motion blur, and temporal resolution. By leveraging these capabilities, the project aims to develop a robust, real-time SLAM system tailored for surgical environments, addressing challenges like high-intensity lighting and head movement-induced motion blur. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project

Generating Realistic Event Camera Data with Generative AI University of Zurich Robotics and Perception In this project, the student applies concepts from current advances in image generation to create artificial events from standard frames. Multiple state-of-the-art deep learning methods will be explored in the scope of this project. Artificial Intelligence and Signal and Image Processing Master Thesis, Semester Project

Enhancing Robotic Motor Policies with Event Cameras University of Zurich Robotics and Perception The goal of this project is to develop a shared embedding space for events and frames, enabling the training of a motor policy on simulated frames and deployment on real-world event data. Artificial Intelligence and Signal and Image Processing Master Thesis, Semester Project

Automatic Failure Detection for Drones University of Zurich Robotics and Perception 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. Engineering and Technology Master Thesis, Semester Project

Vision-based Tracking of Falling Objects University of Zurich Robotics and Perception 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. Engineering and Technology Master Thesis

Event-based Particle Image Velocimetry University of Zurich Robotics and Perception 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. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project

Learning Agile Flight with Nano-drones University of Zurich Robotics and Perception 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. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis

Ultrasonic and Visual Sensor Fusion on Nano-drones University of Zurich Robotics and Perception 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. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis

Events and Frames Fusion for Visual Odometry on Nano-sized Drones University of Zurich Robotics and Perception 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. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis