Fast Reprogramming and Adaptive Reproduction of Contact-rich Assembly

Abstract

Modern manufacturing demands flexible, robust robotic assembly systems capable of handling variable part geometries and dynamic task configurations. Current approaches often suffer from limited generalization, high sample complexity, and the need for extensive reconfiguration or retraining when task parameters change. This paper addresses these limitations by introducing a novel framework that enables adaptive reproduction of kinesthetically taught, contact-rich assembly policies, using only force/torque and proprioceptive sensing. The approach combines three components: (i) synchronized wrench–motion Dynamic Movement Primitives (wDMPs) that encode coupled motion and wrench profiles from a single demonstration; (ii) an uncertainty-aware Model Predictive Controller (MPC) that updates its model online to enable compliant and adaptive contact handling using uncertainty estimated via a Gaussian Mixture Model (GMM); and (iii) a neural contact classifier based on Adaptive Resonance Theory (ART) that distinguishes intended contacts from unintended misalignments and coordinates transitions between assembly stages. Trained on just two demonstrations, one kinesthetic teaching and one assisted successful reproduction, the framework was evaluated on standard benchmarks and real-world industrial scenarios, including peg-in-hole, plug-insertion, and disc brake assemblies. Across 47 assemblies, our framework increased the success rate from 29.8% to 83% in comparison to a classic, non-adaptive compliant controller, and demonstrated improved robustness and transferability over baseline controllers under geometric and pose variations. This contributes towards enabling agile, customizable production with minimal reprogramming effort.