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VL-MemKnG: Hybrid Memory with a Spatio-Temporal Knowledge Graph for Question Answering over Long Egocentric Navigation Trajectories
Svetlana Lukina, Mohamad Al Mdfaa, Gloria Haro +2
Answering navigation-relevant questions over long egocentric videos requires retrieving and organizing evidence distributed across distant temporal moments while maintaining spatia…
DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation
Danil Tokhchukov, Veronika Morozova, Gonzalo Ferrer
Traditional Simultaneous Localization and Mapping (SLAM) algorithms rely heavily on the static environment assumption, which severely limits their applicability in real-world space…
Spatiotemporal Knowledge Graphs as Persistent Scene Memory for Embodied Question Answering
Mohamad Al Mdfaa, Svetlana Lukina, Timur Akhtyamov +4
Vision-language models (VLMs) demonstrate strong image-level scene understanding, but reasoning over long egocentric video remains costly: because VLMs maintain no persistent memor…
PixelNav: Towards Model-based Vision-Only Navigation with Topological Graphs
Sergey Bakulin, Timur Akhtyamov, Denis Fatykhov +2
This work proposes a novel hybrid approach for vision-only navigation of mobile robots, which combines advances of both deep learning approaches and classical model-based planning…
EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild
Timur Akhtyamov, Mohamad Al Mdfaa, Javier Antonio Ramirez Benavides +11
Data-driven navigation algorithms are critically dependent on large-scale, high-quality real-world data collection for successful training and robust performance in realistic and u…