3 papers
cs.RO2026
Normalizing Flows are Capable Models for Bi-manual Visuomotor Policy
Jialong Li, Simon Kristoffersson Lind, Wenrui Xie +2
The field of general-purpose robotics has recently embraced powerful probabilistic diffusion-based models to learn the complex embodiment behaviours. However, existing models often…
cs.CV2024
Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients
Simon Kristoffersson Lind, Rudolph Triebel, Volker Krüger
Modern robotic perception is highly dependent on neural networks. It is well known that neural network-based perception can be unreliable in real-world deployment, especially in di…
cs.LG2024
Uncertainty Quantification Metrics for Deep Regression
Simon Kristoffersson Lind, Ziliang Xiong, Per-Erik Forssén +1
When deploying deep neural networks on robots or other physical systems, the learned model should reliably quantify predictive uncertainty. A reliable uncertainty allows downstream…