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Although a few robots such as vacuum cleaning robots (Jones, 2006; Zhang et al., 2006), lawn mowing robots (Husqvarna; Friendlyrobotics), and some toy robots (Takara; Hasbro) have single functions or perform simple tasks, almost all other service robots perform diverse and complex tasks. Such robots share their work domains with humans, with whom they must constantly interact. In fact, the complexity of the tasks performed by such robots is a result of their interactions with humans. For example, consider a scenario wherein a robot is required to fetch and carry beverages: the methods of delivery are numerous and vary depending on user requirements such as type of beverage, the needs for a container, etc. For a robot designed to control various household devices such as illuminators, windows, television, and other appliances, several services must be provided in various situations; hence, a question-and-answer interaction or some method to infer the necessity of the services is required. For service robots to perform these complex behaviors and collaborate with humans, the programming of robot behavior has been proposed as a natural solution (Knoop et al., 2008). Robot behavior can be programmed manually using text-based and graphical systems, or automatically by demonstration or instructive systems (Biggs & MacDonald, 2003). Recently, many researchers have proposed methods for a service robot to learn high-level tasks. The two main methods are (1) learning by observing human behaviors (Argall et al., 2009) and (2) learning by using procedures defined by humans (a support system can be used to define these procedures) (Lego, 2003; Ekvall et al., 2006).. Manual programming systems are more efficient in creating procedures necessary to cope with various interactive situations than automatic programming systems since the latter require demonstrations and advice for every situation. However, in the process of programming behavior, there exist sub-optimalities (Chen & Zelinsky, 2003), and manual programming systems are more brittle than automatic programming systems. The sub-optimalities of manual programming systems are as follows: (a) in the writing process, humans can make syntactic errors when describing task procedures. For example, writers often misspell the names of actions or important annotations. However, if the errors do not alter the semantic meaning, the problem can be prevented by writing support Source: Human-Robot Interaction, Book edited by: Daisuke Chugo, ISBN 978-953-307-051-3, pp. 288, February 2010, INTECH, Croatia, downloaded from SCIYO.COM

Handling Manually Programmed Task Procedures in Human–Service Robot Interactions