Max Planck ETH Center for Learning Systems

Open Opportunities

Stanford – UC Berkeley Collaboration: Learning Progress Driven Reinforcement Learning for ANYmal ETH Zurich Robotic Systems Lab TLDR: Improving navigation capabilities of ANYmal - RL is simulation - optimizing learning progress. Computer Hardware, Computer Perception, Memory and Attention, Computer Vision, Electrical Engineering, Intelligent Robotics, Robotics and Mechatronics Master Thesis, Semester Project

Visual Language Models for Long-Term Planning ETH Zurich Robotic Systems Lab This project uses Visual Language Models (VLMs) for high-level planning and supervision in construction tasks, enabling task prioritization, dynamic adaptation, and multi-robot collaboration for excavation and site management. prioritization, dynamic adaptation, and multi-robot collaboration for excavation and site management Information, Computing and Communication Sciences Master Thesis, Semester Project

AI Agents for Excavation Planning ETH Zurich Robotic Systems Lab Recent advancements in AI, particularly with models like Claude 3.7 Sonnet, have showcased enhanced reasoning capabilities. This project aims to harness such models for excavation planning tasks, drawing parallels from complex automation scenarios in games like Factorio. We will explore the potential of these AI agents to plan and optimize excavation processes, transitioning from simulated environments to real-world applications with our excavator robot. Engineering and Technology Master Thesis, Semester Project

Transcatheter Heart Valve Repair and Replacement Devices at Harvard Medical School ETH Zurich Multiscale Robotics Lab Master thesis on novel devices and tools for both valve repair and replacement at Harvard Medical School Engineering and Technology, Medical and Health Sciences Master Thesis

Autonomous Robotic Cardiac Catheters at Harvard Medical School ETH Zurich Multiscale Robotics Lab We are developing robotic catheters for heart valve repair and for treatment of arrythmias. Engineering and Technology, Medical and Health Sciences Master Thesis

Low-Dose CT Phantom for Neonates & Children – Materials, Manufacturing & Clinical Validation ETH Zurich Multiscale Robotics Lab Three-dimensional medical imaging techniques such as Computed Tomography (CT) and MRI are indispensable in modern clinical workflows. CT utilizes X-rays acquired from multiple angles to reconstruct detailed volumetric patient anatomy data. Due to the harmful effects of ionizing radiation, especially in vulnerable populations such as infants, it is critical to minimize radiation exposure while maintaining diagnostic image quality. Optimizing CT parameters requires systematic studies, yet direct experimentation on infants is ethically and medically unacceptable. This project aims to develop a novel infant head phantom that accurately replicates the radiological properties of an infant’s head. The phantom will serve as a testbed for CT imaging studies, enabling the optimization of scan parameters that balance minimal radiation exposure with high-quality image acquisition tailored for pediatric neuroimaging. Biomedical Engineering, Manufacturing Engineering, Materials Engineering, Mechanical and Industrial Engineering ETH Zurich (ETHZ), Master Thesis, Semester Project

BEV meets Semantic traversability ETH Zurich Robotic Systems Lab Enable Birds-Eye-View perception on autonomous mobile robots for human-like navigation. Computer Vision, Intelligent Robotics, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition, Photogrammetry and Remote Sensing ETH Zurich (ETHZ), Master Thesis

Scene graphs for robot navigation and reasoning ETH Zurich Robotic Systems Lab Elevate semantic scene graphs to a new level and perform semantically-guided navigation and interaction with real robots at The AI Institute. Computer Vision, Engineering and Technology, Intelligent Robotics, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition ETH Zurich (ETHZ), Master Thesis

Better maps of plant functional traits - towards planttraits.earth v2 ETH Zurich Photogrammetry and Remote Sensing (Prof. Schindler) Functional traits describe biophysically relevant properties of plants and form an important basis for understanding ecosystem dynamics and the Earth system. Planttraits.earth has recently produced global high-resolution maps of many plant traits (some of which have never before been mapped globally), by combining field data from plant scientists, crowd-sourced data from citizen scientists, and remote sensing imagery. The present project will develop methods to improve those maps and bring plant trait mapping to the next level. Ecology and Evolution, Information, Computing and Communication Sciences, Photogrammetry and Remote Sensing Master Thesis, Semester Project

Estimating Real-Height Digital Surface Models from a Single RGB Image Using Generative Models ETH Zurich Photogrammetry and Remote Sensing (Prof. Schindler) This thesis investigates the use of generative diffusion models for estimating Digital Surface Models (DSMs) with at least relative surface height from a single RGB image. While DSMs are traditionally derived from stereo imagery, monocular estimation offers a lightweight alternative for applications where only single-view input is available. Building on recent advances in monocular depth estimation, such as DepthAnythingV2 and Marigold, this work explores whether diffusion-based approaches can effectively bridge the gap between relative depth predictions and real-world surface structure. Information, Computing and Communication Sciences, Photogrammetry and Remote Sensing Bachelor Thesis, Master Thesis, Semester Project