Patric Jensfelt

Robots can now navigate safely in unpredictable environments by effectively managing multi-object uncertainties with a novel risk-aware control framework.

Hierarchical distillation with temporal occupancy diffusion enables LiDAR models to achieve unprecedented accuracy in 3D perception tasks.

Radar data, often relegated to a learned feature, can unlock significant gains in 3D multi-object tracking robustness, especially when other sensors falter in adverse conditions or at long ranges.

Forget unreliable two-frame supervision: TeFlow unlocks 33% better scene flow estimation by mining temporally consistent motion cues across multiple frames for self-supervised feed-forward models.