Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Incremental Online Learning in High Dimensions
Neural Computation
Using inaccurate models in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Two-Level Model of Anticipation-Based Motor Learning for Whole Body Motion
Anticipatory Behavior in Adaptive Learning Systems
Learning impedance control of antagonistic systems based on stochastic optimization principles
International Journal of Robotics Research
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
Correlations in state space can cause sub-optimal adaptation of optimal feedback control models
Journal of Computational Neuroscience
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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.