Our Research

We investigate robots that can understand their environment semantically and geometrically, in order to perform manipulation and other safety critical tasks in proximity to humans. This encompasses semantic understanding under open-set conditions, map representations of the environment, active perception and planning, as well as adaptation and continual self-supervised learning.
Method for localizing a mobile construction robot on a construction site using semantic segmentation, construction robot system and computer program product

US Patent App. 18/284,646, 2024

OptXR: Optimization of Maintenance Processes with Extended Reality and Digital Twins

Center for Sustainable Future Mobility Symposium 2024 (CSFM 2024), 2024

SpotLight: Robotic Scene Understanding through Interaction and Affordance Detection

2024

arxivPDFwebsite
Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information

2024

arxivPDF
NeuSurfEmb: A Complete Pipeline for Dense Correspondence-based 6D Object Pose Estimation without CAD Models

2024

arxivPDF
SNI-SLAM: Semantic Neural Implicit SLAM

CVPR 2024

arxivPDFcodeopenaccess.thecvf.com
A 3D Mixed Reality Interface for Human-Robot Teaming

ICRA 2024

arxivPDFcode
Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach

ICRA 2024

arxivPDF
OpenDAS: Domain Adaptation for Open-Vocabulary Segmentation

2024

arxivPDF
Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and Drawer Manipulation in Point Clouds

ICRA 2024

arxivPDFwebsitecode
" Where am I?" Scene Retrieval with Language

2024

arxivPDF
LabelMaker: Automatic Semantic Label Generation from RGB-D Trajectories

3DV 2024

arxivPDFlabelmaker.orgcode
Unsupervised Continual Semantic Adaptation through Neural Rendering

CVPR 2023

doicodewebsite
Uncertainty estimation for planetary robotic terrain segmentation

2023 IEEE Aerospace Conference, 1-8, 2023