Lessons Learnt from Developing the Embodied AI Platform CAESAR for Domestic Service Robotics

In this paper we outline the development of \Caesar{}, a domestic service robot with which we participated in the robot competition RoboCup@Home for many years. We sketch the system components, in particular the parts relevant to the high-level reasoning system, that make CAESAR an intelligent robot. We report on the development and discuss the lessons we learnt over the years designing, developing and maintaining an intelligent service robot. From our perspective of having participated in RoboCup@Home for a long time, we answer the core questions of the workshop about platforms, challenges and the evaluation of integrative research.

[1]  Ross A. Knepper,et al.  Herb 2.0: Lessons Learned From Developing a Mobile Manipulator for the Home , 2012, Proceedings of the IEEE.

[2]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[4]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[5]  J. McCarthy.  Situations, Actions, and Causal Laws , 1963 .

[6]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[7]  Alexander Ferrein,et al.  Logic-based robot control in highly dynamic domains , 2008, Robotics Auton. Syst..

[8]  James J. Kuffner,et al.  OpenRAVE: A Planning Architecture for Autonomous Robotics , 2008 .

[9]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[10]  Xiaojun Wu,et al.  Quasi Monte Carlo localization for mobile robots , 2012, 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV).

[11]  Alexander Ferrein,et al.  Approaching A Formal Soccer Theory FromBehaviour Specifi Cations In Robotic Soccer , 2008 .

[12]  Mei-Yuh Hwang,et al.  The SPHINX-II speech recognition system: an overview , 1993, Comput. Speech Lang..

[13]  Yang Li,et al.  Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes , 2007, UIST.

[14]  Gerhard Lakemeyer,et al.  Combining Sound Localization and Laser-based Object Recognition , 2007, AAAI Spring Symposium: Multidisciplinary Collaboration for Socially Assistive Robotics.

[15]  Alexander Ferrein,et al.  Reasoning with Qualitative Positional Information for Domestic Domains in the Situation Calculus , 2012, J. Intell. Robotic Syst..

[16]  Alexander Ferrein,et al.  Football is coming home , 2006, PCAR '06.

[17]  Alexander Ferrein,et al.  A Lua-based Behavior Engine for Controlling the Humanoid Robot Nao , 2009, RoboCup.

[18]  Hector J. Levesque,et al.  GOLOG: A Logic Programming Language for Dynamic Domains , 1997, J. Log. Program..

[19]  Alexander Ferrein,et al.  Design Principles of the Component-Based Robot Software Framework Fawkes , 2010, SIMPAR.

[20]  Alexander Ferrein,et al.  Caesar: an intelligent domestic service robot , 2012, Intell. Serv. Robotics.

[21]  Luca Iocchi,et al.  RoboCup@Home: Scientific Competition and Benchmarking for Domestic Service Robots , 2009 .