On Internal Knowledge Representation for Programming Mobile Robots by Demonstration

Intuitive learning of new behaviours is one of the important aspects of social robotics. Among various robot learning approaches, recently Programming by Demonstration (PbD) has gained significant recognition with a lot of potential. Internal representation of the knowledge is a key design choice in the learning process. Using machine learning techniques such as ANNs, HMMs and NARMAX models, simple skills can be encoded from raw sensory data. However, the abstract symbolic representations have demonstrated greater potential for learning complicated tasks but with less details and require a piece of prior knowledge as well. For a particular application, appropriate choice of the symbols is a key design issue. This paper discusses the choice of the symbols to build a PbD process for typical indoor navigation. The learning results are presented for a few tasks to demonstrate the potential of the proposed approach.

[1]  Philip T. Cox,et al.  Programming an Autonomous Robot Controller by Demonstration Using Artificial Neural Networks , 2004, 2004 IEEE Symposium on Visual Languages - Human Centric Computing.

[2]  Danica Kragic,et al.  Task Learning Using Graphical Programming and Human Demonstrations , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.

[3]  Stephen A. Billings,et al.  Robot programming by demonstration through system identification , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Bruce A. MacDonald,et al.  An intuitive interface for a cognitive programming by demonstration system , 2008, 2008 IEEE International Conference on Robotics and Automation.

[5]  Sylvain Calino,et al.  Robot programming by demonstration : a probabilistic approach , 2009 .

[6]  Aude Billard,et al.  On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Danica Kragic,et al.  Layered HMM for Motion Intention Recognition , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Bruce A. MacDonald,et al.  Distance indexed trajectory generation for a helicopter robot for programming by demonstration , 2009, 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[9]  Bruce A. MacDonald,et al.  Retirement home staff and residents' preferences for healthcare robots , 2009, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication.

[10]  Rüdiger Dillmann,et al.  Learning sequential constraints of tasks from user demonstrations , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[11]  Kevin Yoon,et al.  Teaching procedural flow through dialog and demonstration , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Richard M. Voyles,et al.  Automatic training data selection for sensorimotor primitives , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[13]  Rajesh P. N. Rao,et al.  Towards a Real-Time Bayesian Imitation System for a Humanoid Robot , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[14]  Rüdiger Dillmann,et al.  Automatic robot programming from learned abstract task knowledge , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Bruce A. MacDonald,et al.  Robust trajectory segmentation for programming by demonstration , 2009, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication.

[16]  Cédric Hartland,et al.  Using echo state networks for robot navigation behavior acquisition , 2007, 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[17]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[18]  Rüdiger Dillmann,et al.  Towards Cognitive Robots: Building Hierarchical Task Representations of Manipulations from Human Demonstration , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[19]  Stefan Schaal,et al.  Robot Programming by Demonstration , 2009, Springer Handbook of Robotics.

[20]  Stefano Caselli,et al.  Trajectory clustering and stochastic approximation for robot programming by demonstration , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.