  We present Perceive-Represent-Generate (PRG), a novel three-stage framework
that maps perceptual information of different modalities (e.g., visual or
sound), corresponding to a sequence of instructions, to an adequate sequence of
movements to be executed by a robot. In the first stage, we perceive and
pre-process the given inputs, isolating individual commands from the complete
instruction provided by a human user. In the second stage we encode the
individual commands into a multimodal latent space, employing a deep generative
model. Finally, in the third stage we convert the multimodal latent values into
individual trajectories and combine them into a single dynamic movement
primitive, allowing its execution in a robotic platform. We evaluate our
pipeline in the context of a novel robotic handwriting task, where the robot
receives as input a word through different perceptual modalities (e.g., image,
sound), and generates the corresponding motion trajectory to write it, creating
coherent and readable handwritten words.

Robotics;Artificial Intelligence;Machine Learning