30 citations · 179 across the 20 of their papers we have counts for
4 papers · 1 filter
Port-Hamiltonian Modeling of Ideal Fluid Flow: Part II. Compressible and Incompressible Flow
Ramy Rashad, Federico Califano, Frederic P. Schuller +1
Part I of this paper presented a systematic derivation of the Stokes Dirac structure underlying the port-Hamiltonian model of ideal fluid flow on Riemannian manifolds. Starting fro…
Port-Hamiltonian Modeling of Ideal Fluid Flow: Part I. Foundations and Kinetic Energy
Ramy Rashad, Federico Califano, Frederic P. Schuller +1
In this two-parts paper, we present a systematic procedure to extend the known Hamiltonian model of ideal inviscid fluid flow on Riemannian manifolds in terms of Lie-Poisson struct…
Low Dimensional State Representation Learning with Reward-shaped Priors
Nicolò Botteghi, Ruben Obbink, Daan Geijs +5
Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal polic…
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach
Nicolò Botteghi, Beril Sirmacek, Khaled A. A. Mustafa +2
We present a map-less path planning algorithm based on Deep Reinforcement Learning (DRL) for mobile robots navigating in unknown environment that only relies on 40-dimensional raw…