Use the Force, Bot! - Force-Aware ProDMP with Event-Based Replanning

Abstract

Movement Primitives (MPs) are a well-established method for representing and generating modular robot trajectories. This work presents FA-ProDMP, a novel approach that introduces force awareness to Probabilistic Dynamic Movement Primitives (ProDMP). FA-ProDMP adapts trajectories during runtime to account for measured and desired forces, offering smooth trajectories and capturing position and force correlations across multiple demonstrations. FA-ProDMPs support multiple axes of force, making them agnostic to Cartesian or joint space control. This versatility makes FA-ProDMP a valuable tool for learning contact rich manipulation tasks, such as power plug insertion. To reliably evaluate FA-ProDMP, this work additionally introduces a modular, 3D printed task suite called POEMPEL, inspired by the popular Lego Technic pins. POEMPEL mimics industrial peg-in-hole assembly tasks with force requirements and offers multiple parameters of adjustment, such as position, orientation and plug stiffness level, thereby varying the direction and amount of required forces. Our experiments demonstrate that FA-ProDMP outperforms other MP formulations on the POEMPEL setup and a electrical power plug insertion task, thanks to its replanning capabilities based on measured forces. These findings highlight how FA-ProDMP enhances the performance of robotic systems in contact-rich manipulation tasks.