Divide et Impera: Decoding Impedance Strategies for Robotic Peg-in-Hole Assembly

TL;DR

This paper presents a structured analysis of impedance strategies for robotic peg-in-hole assembly, using clustering and PCA to identify patterns, and introduces a neural network predictor to streamline impedance tuning.

Contribution

It introduces a novel analysis of impedance solution distributions, revealing task-specific patterns and providing a neural network for predicting feasible impedance parameters.

Findings

Successful impedance solutions form identifiable clusters.

Task-specific impedance patterns are consistent across different peg types.

Neural network accurately predicts feasible impedance parameters.

Abstract

This paper investigates robotic peg-in-hole assembly using the Elementary Dynamic Actions (EDA) framework, which models contact-rich tasks through a combination of submovements, oscillations, and mechanical impedance. Rather than focusing on a single optimal parameter set, we analyze the distribution and structure of multiple successful impedance solutions, revealing patterns that guide impedance selection in contactrich robotic manipulation. Experiments with a real robot and four different peg types demonstrate the presence of task-specific and generalized assembly strategies, identified through K-means Clustering. Principal Component Analysis (PCA) is used to represent these findings, highlighting patterns in successful impedance selections. Additionally, a neural-network-based success predictor accurately estimates feasible impedance parameters, reducing the need for extensive trial-and-error tuning. By providing publicly available code, CAD files, and a trained model, this work enhances the accessibility of impedance control and offers a structured approach to programming robotic assembly tasks, particularly for less-experienced users.