Adaptive Optimal Control for Redundantly Actuated Arms
From Animals to Animats 10
Date
2008Author
Mitrovic, Djordje
Klanke, Stefan
Vijayakumar, Sethu
Metadata
Abstract
Optimal feedback control has been proposed as an attractive
movement generation strategy in goal reaching tasks for anthropomorphic
manipulator systems. Recent developments, such as the iterative
Linear Quadratic Gaussian (iLQG) algorithm, have focused on the case
of non-linear, but still analytically available, dynamics. For realistic control
systems, however, the dynamics may often be unknown, difficult to
estimate, or subject to frequent systematic changes. In this paper, we
combine the iLQG framework with learning the forward dynamics for
a simulated arm with two limbs and six antagonistic muscles, and we
demonstrate how our approach can compensate for complex dynamic
perturbations in an online fashion.