Sharing and Trading in a Human-Robot System

With the functions of physical robots now extended beyond academia into factories, homes and fields, the interactions between humans and robots have become increasingly extensive and ubiquitous (Haegele et al. 2001). The current state of human interaction with robots in comparison to simple “machines” that operate in structured environment, such as manufacturing automation, is quite different. Robots differ from simple machines in that they are mobile. Some may be autonomous and their actions are not predictable in advance. Hence, there is a need to look into different interaction roles between humans and robots. The issue of interaction roles is an emerging research area in robotics namely Human-Robot Interaction (HRI) (Murphy and Rogers 2001). HRI can be broadly defined as “the study of the humans, robots, and the ways they influence each other” (Fong et al. 2001b). To provide realistic experimental settings, researchers working in this area need to develop Human-Robot System (HRS) to facilitate the study of HRI (Murphy and Rogers 2001). Here, HRS is defined as a “mixed system in which both human and physical robot interact, each as a cooperative intelligent entity” (Hancock 1992). In the context of HRI, an important concern is how human and robot cooperate in a HRS (Sheridan 1992; Murphy and Rogers 2001). In remote operation applications such as space explorations, military operations, automated security, search and rescue, etc., the human does not have direct visual awareness of the environment to perform the required tasks. In these applications, a tight interaction between the human and the robot is required for effective cooperation and coordination. This raises an interaction dilemma: on one hand the robot operating in the remote environment can be expected in a “better position” to advise/inform the human regarding navigation issues (i.e. react locally to the remote environment) and refuses consent to dangerous human commands (e.g. running into obstacles); on the other hand, due to its limited ontologies, the robot requires human assistance on tasks such as object recognition, decision-making, and so forth. Here, limited ontology means that the robot is not able to use constraints either from its knowledge-base or from the environment to control its unspecified parameters. To overcome the above dilemma, adapting to appropriate roles that exploit the capabilities of both human and robot as well as crafting natural and effective modes of interaction are important to create a cooperative HRS. To this end, innovative paradigms have been proposed over the years to redefine the roles of human and robot from the traditional master-slave relationship (Hancock 1992; Sheridan 1992), such as to model the human as cooperator (e.g. Lee 1993; Bourhis and Agostini 1998; Fong et al. 2001b; Hoppenot and Colle 2002; Bruemmer 2003) rather than just as the master controller of the robot. On the other hand, the slave robot is modelled in such a way that it becomes an active assistant