20 citations · 150 across the 43 of their papers we have counts for
3 papers · 1 filter
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze
Diego Romeres, Devesh Jha, Alberto Dalla Libera +2
This paper presents a problem of model learning for the purpose of learning how to navigate a ball to a goal state in a circular maze environment with two degrees of freedom. The m…
Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics
Jeroen van Baar, Alan Sullivan, Radu Cordorel +3
Learning robot tasks or controllers using deep reinforcement learning has been proven effective in simulations. Learning in simulation has several advantages. For example, one can…
Deep Neural Network Inverse Design of Integrated Nanophotonic Devices
Mohammad H. Tahersima, Keisuke Kojima, Toshiaki Koike-Akino +4
Predicting physical response of an artificially structured material is of particular interest for scientific and engineering applications. Here we use deep learning to predict opti…