Enhancing Learning Capabilities of Movement Primitives under Distributed Probabilistic Framework for Assembly Tasks

This paper presents a novel distributed probabilistic framework based on movement primitives for flexible robots assembly implementation. Since modern advanced industrial cell usually deals with various tasks that are not fixed via-point trajectories but highly reconfigurable application templates, the industrial robots used in these applications must be capable of adapting and learning new skills on-demand, without programming experts. Therefore, we propose a probabilistic framework that could accommodate various learning abilities trained with different movement-primitive datasets, separately. Thanks to the fusion theory of the Bayesian Committee Machine, this framework could infer new adapting trajectories with weighted contributions of every trained datasets. To verify the feasibility of our proposed imitation learning framework, state-of-the-art movement learning framework Task-parameterized GMM is compared from several crucial aspects, such as generalization capability, accuracy and robustness. Moreover. this framework is further tested on the YUMI collaborative robot with a rivet picking assembly scenario. Potential applications can be extended to more complicated industrial assembly manufacturing or service robotic applications.