Abstract
Learning from experiences is an effective approach to enhance robot’s competence. This paper focuses on developing capabilities for a robot to obtain robot activity experiences and conceptualize the experiences as plan schemata, which are used as heuristics for the robot to make plans in similar situations. The plan-based robot activity experiences are obtained through human-robot interactions where a teaching action from a command-line user interface triggers recording of an experience. To represent human-robot interaction activities, ontologies for experiences and user instructions are integrated into a robot ontology. Recorded experiences are episodic descriptions of the robot’s activities including relevant perceptions of the environment, the goals pursued, successes, and failures. Since the amount of experience data is large, a graph simplification algorithm based on ego networks is investigated to filter out irrelevant information in an experience. Finally, an approach to robot activity conceptualization based on deductive generalization and abstraction is presented. The proposed system was demonstrated in a scenario where a PR2 robot is taught how to “serve a coffee” to a guest, in the EU project RACE.
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Notes
- 1.
RACE is funded by the EU Seventh Framework Program theme FP7-ICT-2011-7, grant agreement No. 287752.
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Acknowledgments
We would like to thank the other RACE project partners for their efforts in the integration and the demonstrations, and especially to the Technical Aspects of Multimodal Systems (TAMS) group, University of Hamburg, for making the PR2 robot available do the project.
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Mokhtari, V., Lim, G.H., Seabra Lopes, L., Pinho, A.J. (2016). Gathering and Conceptualizing Plan-Based Robot Activity Experiences. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_72
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