Raul Fernandez-Fernandez — accepted AI-conference papers

2022

Neural Style Transfer with Twin-Delayed DDPG for Shared Control of Robotic Manipulators

Raul Fernandez-Fernandez, Marco Aggravi, Paolo Robuffo Giordano, Juan G. Victores, Claudio Pacchierotti

ICRA 2022poster

Neural Style Transfer (NST) refers to a class of algorithms able to manipulate an element, most often images, to adopt the appearance or style of another one. Each element is defined as a combination of Content and Style: the Content can be conceptually defined as the “what” and the Style as the “ho…

Cited by 7 Source ↗Scholar ↗

2018

Robot Imitation Through Vision, Kinesthetic and Force Features with Online Adaptation to Changing Environments

Raul Fernandez-Fernandez, Juan G. Victores, David Estevez, Carlos Balaguer

IROS 2018poster

Continuous Goal-Directed Actions (CGDA)is a robot imitation framework that encodes actions as the changes they produce on the environment. While it presents numerous advantages with respect to other robot imitation frameworks in terms of generalization and portability, final robot joint trajectories…

Cited by 3 Source ↗Scholar ↗

2017

Robotic ironing with 3D perception and force/torque feedback in household environments

David Estevez, Juan G. Victores, Raul Fernandez-Fernandez, Carlos Balaguer

IROS 2017poster

As robotic systems become more popular in household environments, the complexity of required tasks also increases. In this work we focus on a domestic chore deemed dull by a majority of the population, the task of ironing. The presented algorithm improves on the limited number of previous works by j…

Cited by 20 Source ↗code ↗Scholar ↗