Interactive Task Planning through Multiple Abstraction: Application to Assistant Robotics

Assistant robotics has become an emergent field within the robotic and artificial intelligence communities ([1],[2]). The main characteristic of assistant robots is that they are designed to serve non-expert people within their environment. They must plan and act efficiently to accomplish tasks specified in a human-like manner, i.e. "take this envelope to Peter’s office". Thus, an assistant robot must manage a symbolic model of its environment -its world modelthat involves human concepts. A human-inspired world representation can be used to endow assistant robots with that mentioned capability. It is stated in literature that humans widely use the mechanism of abstraction ([3],[6],[8]). Abstraction allows humans to work with abstract concepts that group other, more particular concepts. For instance, when somebody refers to "my office" she/he is talking about an abstract concept, dropping unnecessary details like door, wall, table, chair, cabinet, etc. A robot managing a world model that includes human concepts allows users to specify tasks in a human-like manner. Furthermore, the user can also interact with the robot during the planning process since the robot reports an understandable plan to the human. The work presented in this paper is intended to jointly cover human interaction and task planning efficiency, by using a hierarchical model of the environment. For our purposes, a multihierarchical world model called Multi-AH-graph ([6],[7]) has been implemented with two hierarchies of abstraction: the task planning hierarchy devoted to efficient task planning and the cognitive hierarchy engaged in human communication. The use of these two hierarchies requires a translation process to transform concepts from one hierarchy to the other, which is addressed in this paper. The paper is structured as follows. Section 2 briefly reviews the Multi-AH-graph model. Section 3 is devoted to describe the human interaction mechanism in robot task planning and its application to a real robotic application. Finally, some conclusions and future work are outlined.