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

Bappaditya Debnath, Amir Masoud Ghalamzan Esfahani

Abstract: This paper presents a novel probabilistic approach to deep robot learning from demonstrations (LfD). Deep move-ment 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 real-robot task of strawberry harvesting in the lab. The experimental results demonstrate that latent space learning can significantly improve model prediction performances. The proposed approach allows to sample trajectories …