# Extended Friction Models for the Physics Simulation of Servo Actuators

arXiv: https://arxiv.org/abs/2410.08650 · HTML: https://mlsanity.com/paper/2410.08650/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2024.

## Built on by (most cited first, 3 of 3)

- [First Order Model-Based RL through Decoupled Backpropagation](https://mlsanity.com/paper/2509.00215/) (2025)
- [Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection](https://mlsanity.com/paper/2603.21853/) (2026)
- [Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning](https://mlsanity.com/paper/2607.20399/) (2026)

## Abstract (arXiv)

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.