We are building a framework for large-scale, semantically-aware Gaussian splats to be used in robotic planning. By leveraging scene graphs and shared Gaussian embeddings, our system allows for dynamic, task-driven Level-of-Detail filtering and efficient querying by Vision-Language Models. This approach will be deployed on an ANYmal legged robot in challenging outdoor environments, with a XR pilot interacting with the semantic scene.
Marco Hutter is a Professor of Robotic Systems and the Head of the Center for Robotics at ETH Zurich. His research interests are focused on the development of novel machines and their intelligence for use in harsh and demanding environments. Together with his team, he has developed a range of walking robots, mobile manipulators, and autonomous excavators utilized in industrial inspection, construction and forestry, as household aids, and even for extraterrestrial research. Additionally, Marco is a co-founder of several ETH startups, such as ANYbotics AG and Gravis Robotics AG, which specialize in marketing legged robots and autonomous construction equipment.
Federico Tombari is a Research Director at Google Zurich, Switzerland, where he leads an applied research team in Computer Vision and Machine Learning across the US, Switzerland, and Germany. He is also affiliated with the Faculty of Computer Science at TUM as a lecturer (PrivatDozent) at the CAMP Chair. An up-to-date list of publications is available at his Google Scholar. Federico Tombari's research activity is focused on different aspects of computer vision and machine learning, with a focus on 3D computer vision (e.g. scene understanding, 3D object recognition, 3D reconstruction, SLAM). The fields of application of his research are mainly in robotics, augmented reality, autonomous driving and healthcare. He is currently particularly excited about unsupervised learning for visual data, Large Multimodal Models, (Neural) Radiance Fields, and scene graphs for scene understanding.
