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arXiv · 1807.11541

Markerless Visual Robot Programming by Demonstration

Raphael Memmesheimer·Ivanna Mykhalchyshyna·Viktor Seib·Nick Theisen·Dietrich Paulus
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Abstract

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 informations 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.

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Raphael Memmesheimer, Ivanna Mykhalchyshyna, Viktor Seib, Nick Theisen, Dietrich Paulus. 2018-07-30. Markerless Visual Robot Programming by Demonstration. https://arxiv.org/abs/1807.11541

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