Institute of Robotics and Intelligent Systems D-HEST

Open Opportunities

Autonomous Drone Navigation via Learning from YouTube Videos University of Zurich Robotics and Perception Inspired by how humans learn, this project aims to explore the possibility of learning flight patterns, obstacle avoidance, and navigation strategies by simply watching drone flight videos available on YouTube. Computer Vision 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

RL-based Navigation with Multi-Resolution Semantic Volumetric Maps ETH Zurich Robotic Systems Lab Other organizations: Autonomous Systems Lab Wavemap is a multi-resolution volumetric mapping framework. By integrating its 3D maps with RL pipelines, we want to enable robots to navigate in complex environments. Future semantic support will enable advanced applications like urban navigation and safe traversal of hazardous terrains. Intelligent Robotics Master Thesis

Enhancing Neural Scene Reconstruction through Multimodal Fusion University of Zurich Robotics and Perception The project aims to explore how prior 3D information can assist in reconstructing fine details in NeRFs and how the help of high-temporal resolution data can enhance modeling in the case of scene and camera motion. Computer Vision 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

Fine-tuning Policies in the Real World with Reinforcement Learning University of Zurich Robotics and Perception Explore online fine-tuning in the real world of sub-optimal policies. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project

Inverse Reinforcement Learning from Expert Pilots University of Zurich Robotics and Perception Use Inverse Reinforcement Learning (IRL) to learn reward functions from previous expert drone demonstrations. Engineering and Technology, Intelligent Robotics Master Thesis, Semester Project

Language-guided Drone Control University of Zurich Robotics and Perception Explore the use of large vision language models to control a drone. Engineering and Technology, Intelligent Robotics Master Thesis, Semester Project

From Floorplan to Flight University of Zurich Robotics and Perception Drone racing is considered a proxy task for many real-world applications, including search and rescue missions. In such an application, doorframes, corridors, and other features of the environment could be used to as “gates” the drone needs to pass through. Relevant information on the layout could be extracted from a floor plan of the environment in which the drone is tasked to operate autonomously. To be able to train such navigation policies, the first step is to simulate the environment. This project aims to develop a simulation of environments that procedurally generate corridors and doors based on an input floor plan. We will compare model-based approaches (placing objects according to some heuristic/rules) with learning-based approaches, which directly generate the model based on the floorplan. Engineering and Technology, Intelligent Robotics Master Thesis

Vision-based End-to-End Flight with Obstacle Avoidance University of Zurich Robotics and Perception Recent progress in drone racing enables end-to-end vision-based drone racing, directly from images to control commands without explicit state estimation. In this project, we address the challenge of unforeseen obstacles and changes to the racing environment. The goal is to develop a control policy that can race through a predefined track but is robust to minor track layout changes and gate placement changes. Additionally, the policy should avoid obstacles that are placed on the racetrack, mimicking real-world applications where unforeseen obstacles can be present at any time. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis