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

TL;DR

FA-ProDMP introduces force-aware trajectory adaptation in robot movement primitives, enabling smooth, contact-rich task execution with improved performance through event-based replanning based on force measurements.

Contribution

This work presents FA-ProDMP, a novel force-aware extension of ProDMP that incorporates event-based replanning for contact-rich manipulation tasks.

Findings

FA-ProDMP outperforms other MPs on POEMPEL and plug insertion tasks.

The approach effectively captures force-position correlations over multiple demonstrations.

Replanning based on force feedback improves task success rates.

Abstract

Movement Primitives (MPs) are a well-established method for representing and generating modular robot trajectories. This work presents FA-ProDMP, a new approach which introduces force awareness to Probabilistic Dynamic Movement Primitives (ProDMP). FA-ProDMP adapts the trajectory during runtime to account for measured and desired forces. It offers smooth trajectories and captures position and force correlations over multiple trajectories, e.g. a set of human demonstrations. FA-ProDMP supports multiple axes of force and is thus agnostic to cartesian or joint space control. This makes FA-ProDMP a valuable tool for learning contact rich manipulation tasks such as polishing, cutting or industrial assembly from demonstration. In order 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. It offers multiple parameters of adjustment, such as position, orientation and plug stiffness level, thus varying the direction and amount of required forces. Our experiments show that FA-ProDMP outperforms other MP formulations on the POEMPEL setup and a electrical power plug insertion task, due to its replanning capabilities based on the measured forces. These findings highlight how FA-ProDMP enhances the performance of robotic systems in contact-rich manipulation tasks.