System Identification For Constrained Robots

TL;DR

This paper presents a new iterative least squares method for identifying parameters like motor inertia and joint friction in constrained robotic systems, validated on a humanoid robot, improving control performance.

Contribution

It introduces a novel system identification approach tailored for constrained robots, addressing limitations of traditional methods designed for unconstrained systems.

Findings

Validated in simulation and on a real humanoid robot

Improved model-based control tracking performance

Open-source implementation available

Abstract

Identifying the parameters of robotic systems, such as motor inertia or joint friction, is critical to satisfactory controller synthesis, model analysis, and observer design. Conventional identification techniques are designed primarily for unconstrained systems, such as robotic manipulators. In contrast, the growing importance of legged robots that feature closed kinematic chains or other constraints, poses challenges to these traditional methods. This paper introduces a system identification approach for constrained systems that relies on iterative least squares to identify motor inertia and joint friction parameters from data. The proposed approach is validated in simulation and in the real-world on Digit, which is a 20 degree-of-freedom humanoid robot built by Agility Robotics. In these experiments, the parameters identified by the proposed method enable a model-based controller to achieve better tracking performance than when it uses the default parameters provided by the manufacturer. The implementation of the approach is available at https://github.com/roahmlab/ConstrainedSysID.