Our goal is to mesh the symbolic reasoning capabilities of a cognitive model with the constrained optimization possibilities inherent in optimal controls. We plan to develop and test such a system for several different dynamical models in environments of differing certainty and differing efficiency requirements.
The most general definition of Adaptive Control is one which includes any controller whose behavior changes in response to the controlled system's behavior. In practice, this definition is usually restricted to modifying a small number of controller parameters inorder to maintain system stability or global asymptotic stability of the errors during execution of a single trajectory (Sastry and Bodson 1989, for review). Learning Control represents a second level of operation, since it uses Adaptive Con-335 336 Sanger trol to modify parameters during repeated performance trials of a desired trajectory so that future trials result in greater accuracy (Arimoto et al. 1984). In this paper I present a third level called a "Practice Strategy", in which Learning Control is applied to a sequence of intermediate trajectories leading ultimately to the true desired trajectory. I claim that this can significantly increase learning speed and make learning possible for systems which would otherwise become unstable.
Byravan, Arunkumar (University of Washington) | Monfort, Mathew (University of Illinois at Chicago) | Ziebart, Brian (University of Illinois at Chicago) | Boots, Byron (Georgia Institute of Technology) | Fox, Dieter (University of Washington)
Inverse optimal control (IOC) is a powerful approach for learning robotic controllers from demonstration that estimates a cost function which rationalizes demonstrated control trajectories. Unfortunately, its applicability is difficult in settings where optimal control can only be solved approximately. While local IOC approaches have been shown to successfully learn cost functions in such settings, they rely on the availability of good reference trajectories, which might not be available at test time. We address the problem of using IOC in these computationally challenging control tasks by using a graph-based discretization of the trajectory space. Our approach projects continuous demonstrations onto this discrete graph, where a cost function can be tractably learned via IOC. Discrete control trajectories from the graph are then projected back to the original space and locally optimized using the learned cost function. We demonstrate the effectiveness of the approach with experiments conducted on two 7-degree of freedom robotic arms.
In this paper, we develop a hybrid control approach for legged locomotion. We motivate the development of the control architecture using the results of a series of walking, running and obstacle climbing experiments conducted using a six legged robot called HEX. Our initial simulation results indicate the potential stability of the control approach, and our future analytical work should provide the formal proof of these results. I. Introduction It is well known that legged locomotion involves the use of prototypical movements wherein the phase, frequency and amplitude of individual leg motions are related to one another in specific ways. In the literature, such movements are referred to as gaits.