A Novel Dynamic Motion Primitives Framework for Safe Human-Robot Collaboration

Abstract

Learning by demonstration techniques are gaining popularity within the human-robot collaboration (HRC) scenarios. This is because they allow to deeply exploit the versatility of collaborative robots. In this context, dynamic motion primitives (DMPs) have become a standard method for enabling human operators to easily teach tasks to robots. However, DMPs have two main limitations. First, they may encounter difficulties in generalizing some tasks, which can lead to non-intuitive behavior. Second, it is not guaranteed that the output of DMPs is compliant with ISO/TS 15066, which provides guidelines for assessing safety in collaborative scenarios. This work aims to address these two issues by introducing a novel control pipeline. This pipeline leverages a new variant of DMPs, called Swap DMPs (SDMPs), introduced in this work. The SDMPs enable a more intuitive behavior when the robot reproduces the learned task. Subsequently, SDMPs are encoded into a new optimization problem that ensures the robot complies with the Speed and Separation Monitoring (SSM) collaborative mode. The proposed approach has been experimentally validated and compared with traditional DMPs in both simulation and a real scenario, where a UR5e and a human operator collaborate on a polishing task.