Incremental adaptive integration of layers of a hybrid control architecture

Hybrid deliberative-reactive control architectures are a popular and effective approach to the control of robotic navigation applications. However, due to the fundamental differences in the design of the reactive and deliberative layers, the design of hybrid control architectures can pose significant difficulties. We propose a novel approach to improving system-level performance of hybrid control architectures by incrementally improving the deliberative layer's model of the reactive layer's execution of its plans. Incremental supervised learning techniques are employed to learn the model. Quantitative and qualitative results from a physics-based simulator are presented.

[1]  Erann Gat,et al.  Integrating reaction and planning in a heterogeneous asynchronous architecture for mobile robot navigation , 1991, SGAR.

[2]  Maxim Likhachev,et al.  D*lite , 2002, AAAI/IAAI.

[3]  Tucker R. Balch,et al.  AuRA: principles and practice in review , 1997, J. Exp. Theor. Artif. Intell..

[4]  R. Arkin,et al.  Behavioral diversity in learning robot teams , 1998 .

[5]  Sven Koenig,et al.  Fast replanning for navigation in unknown terrain , 2005, IEEE Transactions on Robotics.

[6]  Peter Stone,et al.  Layered Learning in Multiagent Systems , 1997, AAAI/IAAI.

[7]  L.-J. Lin,et al.  Hierarchical learning of robot skills by reinforcement , 1993, IEEE International Conference on Neural Networks.

[8]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.

[9]  Washington Hilton NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE , 1983 .

[10]  Richard T. Vaughan,et al.  The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems , 2003 .

[11]  Ian H. Witten,et al.  Weka-A Machine Learning Workbench for Data Mining , 2005, Data Mining and Knowledge Discovery Handbook.

[12]  Magnus Egerstedt,et al.  A Modular, Hybrid System Architecture for Autonomous, Urban Driving , 2007, J. Aerosp. Comput. Inf. Commun..

[13]  James M. Rehg,et al.  Learning from examples in unstructured, outdoor environments , 2006, J. Field Robotics.

[14]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.

[15]  James S. Albus,et al.  4D/RCS: a reference model architecture for intelligent unmanned ground vehicles , 2002, SPIE Defense + Commercial Sensing.