A Cross-Platform Robotic Architecture for Autonomous Interactive Robots

This paper reports the lowest level of specification of a new cross-platform robotic architecture for HRI applications called EICA. The main contribution of this paper is a thorough analysis of some of the challenges HRI applications impose on the underlying architecture, and the details of the reactive layer of the EICA architecture that was designed to meet those challenges emphasizing how low level attention focusing and action integration is implemented. The paper also describes the implementation of a listener robot that uses human-like nonverbal behavior during explanation scenarios using the proposed architecture and reports some encouraging results from experimenting with a simplified simulation of this robot. 1 REVIEW AND MOTIVATION Many researchers have studied robotic architectures for mobile autonomous robots. The proposed architectures can broadly be divided into reactive, deliberative, or hybrid architectures. One general problem with most hybrid architectures concerning real world interactions is the fixed pre-determined relation between deliberation and reaction [1], [2]. Interaction between humans in the real world utilizes many channels including verbal, and nonverbal channels. To manage those channels, the agent needs to have a variety of skills and abilities including dialog management, synchronization of verbal and nonverbal intended behavior, and efficient utilization of normal society-dependent unintended nonverbal behavior patterns. Those skills are managed in humans using both conscious and unconscious processes of a wide range of computational load. This suggests that implementing such behaviors in a robot will require integration of various technologies ranging from fast reactive processes to long term deliberative operations. The relation between the deliberative and reactive subsystems needed to implement natural interactivity is very difficult to be catched in well structured relations like deliberation as learning, deliberation as configuration, or reaction as advising usually found in hybrid architectures. On the other hand, most other autonomous applications used to measure the effectiveness of robotic architectures (like autonomous indoor and outdoor navigation, collecting empty cans, delivering Faxes, and underwater navigation) require a very well structured