Improving the Reliability of Service Robots by Symbolic Representation of Execution Specific Knowledge
- In the field of service robots, dealing with faults is crucial to promote user acceptance. In this context, this work focuses on some specific faults which arise from the interaction of a robot with its real world environment due to insufficient knowledge for action execution. In our previous work [1], we have shown that such missing knowledge can be obtained through learning by experimentation. The combination of symbolic and geometric models allows us to represent action execution knowledge effectively. However we did not propose a suitable representation of the symbolic model. In this work we investigate such symbolic representation and evaluate its learning capability. The experimental analysis is performed on four use cases using four different learning paradigms. As a result, the symbolic representation together with the most suitable learning paradigm are identified.
| Document Type: | Conference Object |
|---|---|
| Language: | English |
| Author: | Anastassia Kuestenmacher, Paul G. Plöger |
| Parent Title (English): | Robust and Reliable Autonomy in the Wild (R2AW) |
| URL: | http://rbr.cs.umass.edu/r2aw |
| Publication year: | 2021 |
| Departments, institutes and facilities: | Fachbereich Informatik |
| Dewey Decimal Classification (DDC): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Entry in this database: | 2021/09/06 |
