Two Layers Action Integration for HRI - Action Integration with Attention Focusing for Interactive Robots

Behavior architectures are widely used to program interactive robots. In these architectures multiple behaviors are usually running concurrently so a mechanism for integrating the resulting actuation commands from these behaviors into actual actuation commands sent to the robot’s motor system must be faced. Different architectures proposed different action integration mechanisms that range from distributed to central integration. In this paper an analysis of the special requirements that HRI imposes on the action integration system is given. Based on this analysis a novelle hybrid action integration mechanism that combines distributed attention focusing with a fast central integration algorithm is presented in the framework of the EICA architecture. The proposed system was tested in a simulation of a listener robot that aimed to achieve human-like nonverbal listening behavior in real world interactions. The analysis of the system showed that the proposed mechanism can generate coherent human-like behavior while being robust against signal correlated noise.

[1]  P. Maes.  How to Do the Right Thing , 1989 .

[2]  Randall D. Beer,et al.  Biological Neural Networks in Invertebrate Neuroethology and Robotics. Editors: Randall D. Beer, Roy E. Ritzmann, Thomas McKenna (Academic Press, Inc., Harcourt Brace Jovanovich, 1993) , 1996, SGAR.

[3]  Ah-Hwee Tan,et al.  A Hybrid Architecture Combining Reactive Plan Execution and Reactive Learning , 2006, PRICAI.

[4]  Toyoaki Nishida,et al.  A new, HRI inspired, view of intention and intention communication , 2007, AAAI 2007.

[5]  Hector J. Levesque,et al.  Intention is Choice with Commitment , 1990, Artif. Intell..

[6]  Hiroshi Ishiguro,et al.  A robot architecture based on situated modules , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[7]  Marc Carreras Pérez.  A proposal of a behavior-based control architecture with reinforcement learning for an autonomous underwater robot , 2003 .

[8]  Monica N. Nicolescu,et al.  A hierarchical architecture for behavior-based robots , 2002, AAMAS '02.

[9]  Ronald C. Arkin,et al.  Modeling neural function at the schema level: implications and results for robotic control , 1993 .

[10]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[11]  Tetsuo Ono,et al.  A humanoid robot that pretends to listen to route guidance from a human , 2007, Auton. Robots.

[12]  Y. Mohammad,et al.  Embodiment of knowledge into the interaction and physical domains using robots , 2007, 2007 International Conference on Control, Automation and Systems.

[13]  Andrea Lockerd Thomaz,et al.  Effects of nonverbal communication on efficiency and robustness in human-robot teamwork , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.