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This paper presents a novel probabilistic approach to deep robot learningfrom demonstrations (LfD). Deep movement primitives (DMPs) are deterministicLfD model that maps visual information directly into a robot trajectory. Thispaper extends DMPs and presents a deep probabilistic model that maps the visualinformation into a distribution of effective robot trajectories. Thearchitecture that leads to the highest level of trajectory accuracy ispresented and compared with the existing methods. Moreover, this paperintroduces a novel training method for learning domain-specific latentfeatures. We show the superiority of the proposed probabilistic approach andnovel latent space learning in the lab's real-robot task of strawberryharvesting. The experimental results demonstrate that latent space learning cansignificantly improve model prediction performances. The proposed approachallows to sample trajectories from distribution and optimises the robottrajectory to meet a secondary objective, e.g. collision avoidance.

dPMP-Deep Probabilistic Motion Planning: A use case in Strawberry Picking Robot