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
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 how of
said element. In this context, we propose a custom NST framework for
transferring a set of styles to the motion of a robotic manipulator, e.g., the
same robotic task can be carried out in an angry, happy, calm, or sad way. An
autoencoder architecture extracts and defines the Content and the Style of the
target robot motions. A Twin Delayed Deep Deterministic Policy Gradient (TD3)
network generates the robot control policy using the loss defined by the
autoencoder. The proposed Neural Policy Style Transfer TD3 (NPST3) alters the
robot motion by introducing the trained style. Such an approach can be
implemented either offline, for carrying out autonomous robot motions in
dynamic environments, or online, for adapting at runtime the style of a
teleoperated robot. The considered styles can be learned online from human
demonstrations. We carried out an evaluation with human subjects enrolling 73
volunteers, asking them to recognize the style behind some representative
robotic motions. Results show a good recognition rate, proving that it is
possible to convey different styles to a robot using this approach.