Extended Friction Models for the Physics Simulation of Servo Actuators

TL;DR

This paper introduces extended friction models for servo actuators that improve simulation accuracy, facilitating better transfer of control algorithms from simulation to real-world robotics.

Contribution

It presents a new comprehensive analysis and parameter identification method for advanced friction models integrated into physics engines, validated on multiple servo actuators.

Findings

Enhanced simulation accuracy over standard models

Validated models on four servo actuators

Improved transferability of control algorithms

Abstract

Accurate physical simulation is crucial for the development and validation of control algorithms in robotic systems. Recent works in Reinforcement Learning (RL) take notably advantage of extensive simulations to produce efficient robot control. State-of-the-art servo actuator models generally fail at capturing the complex friction dynamics of these systems. This limits the transferability of simulated behaviors to real-world applications. In this work, we present extended friction models that allow to more accurately simulate servo actuator dynamics. We propose a comprehensive analysis of various friction models, present a method for identifying model parameters using recorded trajectories from a pendulum test bench, and demonstrate how these models can be integrated into physics engines. The proposed friction models are validated on four distinct servo actuators and tested on 2R manipulators, showing significant improvements in accuracy over the standard Coulomb-Viscous model. Our results highlight the importance of considering advanced friction effects in the simulation of servo actuators to enhance the realism and reliability of robotic simulations.