4 citations · 8 across the 9 of their papers we have counts for
4 papers · 1 filter
Active Learning of Robot Vision Using Adaptive Path Planning
Julius Rückin, Federico Magistri, Cyrill Stachniss +1
Robots need robust and flexible vision systems to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonom…
Towards Map-Agnostic Policies for Adaptive Informative Path Planning
Julius Rückin, David Morilla-Cabello, Cyrill Stachniss +2
Robots are frequently tasked to gather relevant sensor data in unknown terrains. A key challenge for classical path planning algorithms used for autonomous information gathering is…
Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning
Apoorva Vashisth, Julius Rückin, Federico Magistri +2
Autonomous robots are often employed for data collection due to their efficiency and low labour costs. A key task in robotic data acquisition is planning paths through an initially…
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning
Julius Rückin, Federico Magistri, Cyrill Stachniss +1
Semantic segmentation enables robots to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonomous robots…