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.