Towards Cognitive Robots: Building Hierarchical Task Representations of Manipulations from Human Demonstration

This paper deals with building up a knowledge base of manipulation tasks by extracting relevant knowledge from demonstrations of manipulation problems. Hereby the focus of the paper is on modeling and representing manipulation tasks enabling the system to reason and reorganize the gathered knowledge in terms of reusability, scalability and explainability of learned skills and tasks. The goal is to compare the newly acquired skill or task with already existing task knowledge and decide whether to add a new task representation or to expand the existing representation with an alternative. Furthermore, a constraint for the representation is that at execution time the built knowledge base can be integrated and used in a symbolic planner.

[1]  Tom Michael Mitchell,et al.  Explanation-based generalization: A unifying view , 1986 .

[2]  Robert James Firby,et al.  Adaptive execution in complex dynamic worlds , 1989 .

[3]  Masayuki Inaba,et al.  Learning by watching: extracting reusable task knowledge from visual observation of human performance , 1994, IEEE Trans. Robotics Autom..

[4]  R. James Firby,et al.  The RAP language manual , 1995 .

[5]  Karen L. Myers.  A Procedural Knowledge Approach to Task-Level Control , 1996, AIPS.

[6]  Kunikatsu Takase,et al.  Assembly motion teaching system using position/force simulator-generating control program , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[7]  Alessandro Saffiotti,et al.  The Saphira architecture: a design for autonomy , 1997, J. Exp. Theor. Artif. Intell..

[8]  Reid G. Simmons,et al.  A task description language for robot control , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[9]  Holger Friedrich,et al.  Interaktive Programmierung von Manipulationssequenzen , 1998 .

[10]  Paul Hudak,et al.  A language for declarative robotic programming , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[11]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[12]  Rüdiger Dillmann,et al.  Learning Robot Behaviour and Skills Based on Human Demonstration and Advice: The Machine Learning Paradigm , 2000 .

[13]  Jörg Hoffmann,et al.  FF: The Fast-Forward Planning System , 2001, AI Mag..

[14]  Roland Siegwart,et al.  The interactive autonomous mobile system RoboX , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Chrystopher L. Nehaniv,et al.  Imitation with ALICE: learning to imitate corresponding actions across dissimilar embodiments , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[16]  R. Dillmann,et al.  Programming service tasks in household environments by human demonstration , 2002, Proceedings. 11th IEEE International Workshop on Robot and Human Interactive Communication.

[17]  Rüdiger Dillmann,et al.  Using multiple probabilistic hypothesis for programming one and two hand manipulation by demonstration , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[18]  Monica N. Nicolescu,et al.  Natural methods for robot task learning: instructive demonstrations, generalization and practice , 2003, AAMAS '03.

[19]  Gregory D. Hager,et al.  A Comparative Review of Robot Programming Languages , 2003 .