Data-Driven Dynamic Parameter Learning of manipulator robots

TL;DR

This paper introduces a Transformer-based method for dynamic parameter estimation in manipulators, utilizing automated dataset generation and attention mechanisms to improve accuracy and scalability in sim-to-real transfer.

Contribution

It presents a novel Transformer-based approach combined with automated data generation for accurate, scalable dynamic parameter estimation in robotic manipulators.

Findings

Transformer model achieves high accuracy with R2 of 0.8633

Mass and inertia are estimated with near-perfect accuracy

Coulomb friction estimation is moderately accurate

Abstract

Bridging the sim-to-real gap remains a fundamental challenge in robotics, as accurate dynamic parameter estimation is essential for reliable model-based control, realistic simulation, and safe deployment of manipulators. Traditional analytical approaches often fall short when faced with complex robot structures and interactions. Data-driven methods offer a promising alternative, yet conventional neural networks such as recurrent models struggle to capture long-range dependencies critical for accurate estimation. In this study, we propose a Transformer-based approach for dynamic parameter estimation, supported by an automated pipeline that generates diverse robot models and enriched trajectory data using Jacobian-derived features. The dataset consists of 8,192 robots with varied inertial and frictional properties. Leveraging attention mechanisms, our model effectively captures both temporal and spatial dependencies. Experimental results highlight the influence of sequence length, sampling rate, and architecture, with the best configuration (sequence length 64, 64 Hz, four layers, 32 heads) achieving a validation R2 of 0.8633. Mass and inertia are estimated with near-perfect accuracy, Coulomb friction with moderate-to-high accuracy, while viscous friction and distal link center-of-mass remain more challenging. These results demonstrate that combining Transformers with automated dataset generation and kinematic enrichment enables scalable, accurate dynamic parameter estimation, contributing to improved sim-to-real transfer in robotic systems