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|Title:||ADAPTIVE ROBOT TRAINING EXPLORATIONS IN SENSORLESS MANIPULATION|
|Abstract:||Several different techniques for training robots exist. The simplest and most common is leading or guiding, but this method is limited and inflexible. Textual or explicit programming has enjoyed many advances in the last decade and current research versions of explicit systems are very powerful. But explicit programming is complex and requires the skills of specially trained programmers. Mixed systems attempt to capitalize on the benefits of guiding and programming to simplify the training process. This union is particularly applicable to training sensorless robots in which the robot is used as a measuring device to specify task locations. But existing mixed systems exhibit a poorly designed interface, thus creating problems in the way the programming and guiding processes interact. This thesis presents the design and implementation of a prototypical mixed system that improves not only the programming and guiding components, but also their interface. The improvements are embodied in ART, an Adaptive Robot Trainer. ART's development involved an analysis of mixed systems and assembly tasks that resulted in an effective representation of task state. The representation led to the design of ART's programming language which automates much of the program-guiding interaction. ART's syntax allows the programmer to express assembly operations and object-feature relationships in a natural way while providing the system with the information necessary to maintain task state. The representation also enables guiding error corrections, flexibility in the guiding protocol, and the generation of meaningful messages to prompt operator actions.|
|Appears in Collections:||Technical Reports|
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