System Identification and Control of Valkyrie through SVA--Based Regressor Computation
This work addresses the challenge of accurate and efficient system identification and control for humanoid robots, which is incremental as it builds on existing Spatial Vector Algebra methods with a new algorithm for regressor computation.
The paper tackles the problem of simultaneous identification and control for the humanoid robot Valkyrie by using Spatial Vector Algebra to compute dynamics terms like inertia, Coriolis-centrifugal, and gravity, proposing an algorithm with O(n^2) runtime for online system identification and model-based control, validated through experiments including offline identification of a double pendulum and a 4-DOF robotic leg, and online identification and control of a 4-DOF robotic arm.
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.