A Computer-Based Learning System for Remote Manipulator Control

A concept of adaptive aiding for performance improvement in remote handling is described. The concept incorporates an autonomous control subsystem (ACS) that is able to supplement the operator's control function. The behavior of the ACS is established through a process of learning by observing the operator's control function in relation to the environment and manipulator output. The computer-based system establishes a decision-making policy which is based on conditional probability. Initially, the output device is totally controlled by the operator, while the computer system acts as a passive observer. As the operation continues, the computer system gradually assumes the role of active controller, reducing the operator's function to that of an action initiator and inhibitor. A pilot experiment indicates the feasibility of the concept; with a relatively short training period, the ACS was able to assume the bulk of the decision-making load and guide a three-dimensional manipulator satisfactorily through a series of manipulative tasks.