An Incremental Feature Set Refinement in a Programming by Demonstration Scenario
| Autor: | Jun Miura, Hoai Luu-Duc |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 010401 analytical chemistry Programming by demonstration Feature selection 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Robot learning Human–robot interaction 0104 chemical sciences Task (project management) Set (abstract data type) Simple (abstract algebra) 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | ICARM |
| DOI: | 10.1109/icarm.2019.8833723 |
| Popis: | In transferring knowledge from human to robot using Programming by Demonstration (PbD), choosing features which can represent the instructor demonstrations is an essential part of robot learning. With a relevant set of features, the robot can not only have a better performance but also decrease the learning cost. In this work, the feature selection method is proposed to help the robot determine which subset of the features is relevant to represent a task in PbD framework. We implement an experimental PbD system for a simple task as proofing our concept as well as showing the preliminary results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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