From Abstract Task Knowledge to Executable Robot Programs

Authors:
Steffen Knoop;Michael Pardowitz;Rüdiger Dillmann
Affiliations:
Industrial Applications of Computer Science and Micro Systems (IAIM), Institute of Computer Science and Engineering (CSE), University of Karlsruhe (TH), Karlsruhe, Germany 76131;Industrial Applications of Computer Science and Micro Systems (IAIM), Institute of Computer Science and Engineering (CSE), University of Karlsruhe (TH), Karlsruhe, Germany 76131;Industrial Applications of Computer Science and Micro Systems (IAIM), Institute of Computer Science and Engineering (CSE), University of Karlsruhe (TH), Karlsruhe, Germany 76131
Venue:
Journal of Intelligent and Robotic Systems
Year:
2008

Citing 4
Cited 3

HTN planning: complexity and expressivity

AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Robot instruction by human demonstration

Robot instruction by human demonstration
Natural methods for robot task learning: instructive demonstrations, generalization and practice

AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Adaptive execution in complex dynamic worlds

Adaptive execution in complex dynamic worlds

Desiging robot services with ontology and learning

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Virtual reality and programming by demonstration: Teaching a robot to grasp a dynamic object by the generalization of human demonstrations

Presence: Teleoperators and Virtual Environments
A behaviour sequencing and composition architecture based on ontologies for entertainment humanoid robots

Robotics and Autonomous Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Robots that are capable of learning new tasks from humans need the ability to transform gathered abstract task knowledge into their own representation and dimensionality. New task knowledge that has been collected e.g. with Programming by Demonstration approaches by observing a human does not a-priori contain any robot-specific knowledge and actions, and is defined in the workspace of the human demonstrator. This article presents a new approach for mapping abstract human-centered task knowledge to a robot execution system based on the target system properties. Therefore the required background knowledge about the target system is examined and defined explicitly.