System Identification and Control of Valkyrie through SVA--Based Regressor Computation

TL;DR

This paper introduces a novel SVA-based regressor computation method enabling efficient online system identification and control of humanoid robots, validated through multiple experimental case studies.

Contribution

It presents a new algorithm for SVA-based regressor computation with quadratic runtime, facilitating real-time system identification and control of robots.

Findings

Efficient O(n^2) algorithm for SVA-based regressor computation.

Successful online identification and control of a 4-DOF robotic arm.

Validation through experiments on double pendulum, robotic leg, and robotic arm.

Abstract

This paper demonstrates simultaneous identification and control of the humanoid robot, Valkyrie, utilizing Spatial Vector Algebra (SVA). In particular, the inertia, Coriolis-centrifugal and gravity terms for the dynamics of a robot are computed using spatial inertia tensors. With the assumption that the link lengths or the distance between the joint axes are accurately known, it will be shown that inertial properties of a robot can be directly evaluated from the inertia tensor. An algorithm is proposed to evaluate the regressor, yielding a run time of $O(n^2)$. The efficiency of this algorithm yields a means for online system identification via the SVA--based regressor and, as a byproduct, a method for accurate model-based control. Experimental validation of the proposed method is provided through its implementation in three case studies: offline identification of a double pendulum and a $4$-DOF robotic leg, and online identification and control of a $4$-DOF robotic arm.