  DMP have been extensively applied in various robotic tasks thanks to their
generalization and robustness properties. However, the successful execution of
a given task may necessitate the use of different motion patterns that take
into account not only the initial and target position but also features
relating to the overall structure and layout of the scene. To make DMP
applicable to a wider range of tasks and further automate their use, we design
a framework combining deep residual networks with DMP, that can encapsulate
different motion patterns of a planar task, provided through human
demonstrations on the RGB image plane. We can then automatically infer from new
raw RGB visual input the appropriate DMP parameters, i.e. the weights that
determine the motion pattern and the initial/target positions. We compare our
method against another SoA method for inferring DMP from images and carry out
experimental validations in two different planar tasks.
