Robotic Imitation by Markerless Visual Observation and Semantic Associations
| Autor: | Ivanna Kramer, Raphael Memmesheimer, Viktor Seib, Nick Theisen, Dietrich Paulus |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science 02 engineering and technology computer.file_format Ontology (information science) Object (computer science) Semantics 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Task analysis Robot 020201 artificial intelligence & image processing Computer vision Executable Artificial intelligence business Set (psychology) computer Pose |
| Zdroj: | ICARSC |
| DOI: | 10.1109/icarsc49921.2020.9096123 |
| Popis: | In this paper we present an approach for learning to imitate human behavior on a semantic level by markerless visual observation. We analyze a set of spatial constraints on human pose data extracted using convolutional pose machines and object information extracted from 2D image sequences. A scene analysis, based on an ontology of objects and affordances, is combined with continuous human pose estimation and spatial object relations. Using a set of constraints we associate the observed human actions with a set of executable robot commands. We demonstrate our approach in a kitchen task, where the robot learns to prepare a meal. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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