Iterative Machine Learning for Precision Trajectory Tracking with Series Elastic Actuators

TL;DR

This paper presents an iterative machine learning method using Gaussian Process Regression to improve position tracking accuracy in robots with Series Elastic Actuators, balancing force control and precision.

Contribution

It introduces a novel iterative learning approach that estimates local system models to enhance trajectory tracking in SEAs, addressing a key trade-off in robotic control.

Findings

Successful convergence of the iterative method reduces tracking error.

Improved position accuracy demonstrated on a 2-DOF robotic arm.

Effective use of complex-valued Gaussian Process Regression for model estimation.

Abstract

When robots operate in unknown environments small errors in postions can lead to large variations in the contact forces, especially with typical high-impedance designs. This can potentially damage the surroundings and/or the robot. Series elastic actuators (SEAs) are a popular way to reduce the output impedance of a robotic arm to improve control authority over the force exerted on the environment. However this increased control over forces with lower impedance comes at the cost of lower positioning precision and bandwidth. This article examines the use of an iteratively-learned feedforward command to improve position tracking when using SEAs. Over each iteration, the output responses of the system to the quantized inputs are used to estimate a linearized local system models. These estimated models are obtained using a complex-valued Gaussian Process Regression (cGPR) technique and then, used to generate a new feedforward input command based on the previous iteration's error. This article illustrates this iterative machine learning (IML) technique for a two degree of freedom (2-DOF) robotic arm, and demonstrates successful convergence of the IML approach to reduce the tracking error.