Beyond adaptive-critic creative learning for intelligent mobile robots

Xiaoqun Liao; Ming Cao; Ernest L. Hall

doi:10.1117/12.444207

5 October 2001 Beyond adaptive-critic creative learning for intelligent mobile robots

Xiaoqun Liao, Ming Cao, Ernest L. Hall

Proceedings Volume 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision; (2001) https://doi.org/10.1117/12.444207
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

Abstract

Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it permits the discovery of the unknown problems, ones that are not yet recognized but may be critical to survival or success.

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Xiaoqun Liao, Ming Cao, and Ernest L. Hall "Beyond adaptive-critic creative learning for intelligent mobile robots", Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); https://doi.org/10.1117/12.444207

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KEYWORDS

Robots

Neural networks

Neurons

Computer programming

Mobile robots

Machine learning

Cognitive modeling

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