Institute of Robotics and Intelligent Systems D-HEST

Open Opportunities

Fast Object Detection Using Spiking Neural Networks and Event Cameras for Real-Time Scene Analysis University of Zurich Robotics and Perception This project leverages spiking neural networks (SNNs) and event cameras to create a real-time system for detecting fast-moving objects with high efficiency and minimal latency. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project

Event-Augmented Image-Based Neural Networks for High-Accuracy, Low-Latency Visual Perception University of Zurich Robotics and Perception This project develops a hybrid framework combining the high spatial detail of image-based neural networks with the high temporal resolution of event camera data to achieve accurate, low-latency visual perception. It targets real-time tasks like semantic segmentation and object detection, addressing challenges such as open-vocabulary recognition for dynamic and adaptive applications. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project

Safe RL for Robot Social Navigation ETH Zurich Spinal Cord Injury & Artificial Intelligence Lab Other organizations: Sensory-Motor Systems Lab, ETH Competence Center - ETH AI Center Developing a constrained RL framework for social navigation, emphasizing explicit safety constraints to reduce reliance on reward tuning. Engineering and Technology Master Thesis

Enhancing Vision-Based Drone Racing with Neural-based Rendering Augmentation Techniques University of Zurich Robotics and Perception This project enhances vision-based drone racing by integrating neural rendering and advanced data augmentation techniques to improve policy generalization and robustness in unseen environments. It focuses on developing methods to strengthen gate detection accuracy and overall perception for autonomous drone navigation in dynamic scenarios. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project

Drones for search and rescue in disaster scenarios using RL University of Zurich Robotics and Perception 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. Intelligent Robotics, Robotics and Mechatronics Master Thesis, Semester Project

Leveraging Event Cameras for 3D Gaussian Splatting Reconstruction under fast motion University of Zurich Robotics and Perception This project seeks to leverage the sparse nature of events to accelerate the training of radiance fields. Computer Vision Master Thesis, Semester Project

Efficient Event Stream Encoding Using Spiking Neural Networks for Low-Latency Visual Processing University of Zurich Robotics and Perception In this project we develop spiking neural networks (SNN)-based framework to efficiently compress event camera data, enabling low-latency, power-efficient processing for tasks like classification, object detection, and optical flow prediction. It combines the sparsity and speed of SNNs with the accuracy of ANNs. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project

Advancing Space Navigation and Landing with Event-Based Camera in collaboration with the European Space Agency University of Zurich Robotics and Perception 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. Engineering and Technology Master Thesis

Agile exploration of ballast tank with reinforcement learning University of Zurich Robotics and Perception 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. Intelligent Robotics, Robotics and Mechatronics Master Thesis, Semester Project

Advancing Low-Latency Processing for Event-Based Neural Networks University of Zurich Robotics and Perception Design and implement efficient event-based networks to achieve low latency inference. Computer Vision Master Thesis, Semester Project