15 citations · 43 across the 12 of their papers we have counts for
4 papers · 1 filter
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
Nicolò Botteghi, Luuk Grefte, Mannes Poel +4
Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, t…
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics
Nicolò Botteghi, Mannes Poel, Beril Sirmacek +1
Deep Reinforcement Learning has shown its ability in solving complicated problems directly from high-dimensional observations. However, in end-to-end settings, Reinforcement Learni…
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
Nicolò Botteghi, Khaled Alaa, Mannes Poel +4
Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life. In non-structured environments and in the presence of uncertainties, su…
Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations
Manu Kalia, Steven L. Brunton, Hil G. E. Meijer +2
Complex systems manifest a small number of instabilities and bifurcations that are canonical in nature, resulting in universal pattern forming characteristics as a function of some…