Simone Mentasti

Pedestrians alter their velocity and perception when looking directly at an approaching robot, and incorporating these egocentric awareness cues alongside patch-partitioned occupancy maps in a selective state-space backbone establishes new state-of-the-art accuracy across five navigation benchmarks.

Transforming long-form videos into compact, temporally grounded scene graphs allows MLLMs to maintain semantic richness while overcoming input constraints, leading to state-of-the-art VQA performance.