Julia Hindel

Comprehensive scene understanding and state estimation are pivotal for achieving reliable robot autonomy in human-centred environments. Yet, the availability of reliable training data undermines the robustness - and thus safety - of such systems, especially in edge cases not or barely covered. This project focuses on learning methods without extensive manual human supervision to facilitate efficient learning and transferability of learned models in panoptic segmentation and tracking tasks to enable superior spatio-temporal reasoning. Further, the research is directed toward the fusion of multiple modalities to increase the robustness of predictions in challenging environmental conditions and allow online learning by enforcing consistency among them. Cross-modality of this kind will also be directly focused on introspection, where explainability of failures (a crucial capability for trustworthy autonomous systems) in one sensor stream will be boosted by other sensor streams.