  This paper presents a novel probabilistic approach to deep robot learning
from demonstrations (LfD). Deep movement primitives (DMPs) are deterministic
LfD model that maps visual information directly into a robot trajectory. This
paper extends DMPs and presents a deep probabilistic model that maps the visual
information into a distribution of effective robot trajectories. The
architecture that leads to the highest level of trajectory accuracy is
presented and compared with the existing methods. Moreover, this paper
introduces a novel training method for learning domain-specific latent
features. We show the superiority of the proposed probabilistic approach and
novel latent space learning in the lab's real-robot task of strawberry
harvesting. The experimental results demonstrate that latent space learning can
significantly improve model prediction performances. The proposed approach
allows to sample trajectories from distribution and optimises the robot
trajectory to meet a secondary objective, e.g. collision avoidance.
