  Learning from humans allows non-experts to program robots with ease, lowering
the resources required to build complex robotic solutions. Nevertheless, such
data-driven approaches often lack the ability to provide guarantees regarding
their learned behaviors, which is critical for avoiding failures and/or
accidents. In this work, we focus on reaching/point-to-point motions, where
robots must always reach their goal, independently of their initial state. This
can be achieved by modeling motions as dynamical systems and ensuring that they
are globally asymptotically stable. Hence, we introduce a novel Contrastive
Learning loss for training Deep Neural Networks (DNN) that, when used together
with an Imitation Learning loss, enforces the aforementioned stability in the
learned motions. Differently from previous work, our method does not restrict
the structure of its function approximator, enabling its use with arbitrary
DNNs and allowing it to learn complex motions with high accuracy. We validate
it using datasets and a real robot. In the former case, motions are 2 and 4
dimensional, modeled as first- and second-order dynamical systems. In the
latter, motions are 3, 4, and 6 dimensional, of first and second order, and are
used to control a 7DoF robot manipulator in its end effector space and joint
space. More details regarding the real-world experiments are presented in:
\url{https://youtu.be/OM-2edHBRfc}.
