4 papers
Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning
Arvi Jonnarth, Ola Johansson, Jie Zhao +1
Coverage path planning (CPP) is the problem of finding a path that covers the entire free space of a confined area, with applications ranging from robotic lawn mowing to search-and…
Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning
Arvi Jonnarth, Jie Zhao, Michael Felsberg
Coverage path planning (CPP) is the problem of finding a path that covers the entire free space of a confined area, with applications ranging from robotic lawn mowing to search-and…
Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks
Ziliang Xiong, Arvi Jonnarth, Abdelrahman Eldesokey +3
Computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it…
High-fidelity Pseudo-labels for Boosting Weakly-Supervised Segmentation
Arvi Jonnarth, Yushan Zhang, Michael Felsberg
Image-level weakly-supervised semantic segmentation (WSSS) reduces the usually vast data annotation cost by surrogate segmentation masks during training. The typical approach invol…