Year: 2012
Type: conference-paper
Abstract: Biological research supports the concept that advanced motion emerges from modular building blocks, which generate both rhythmical and discrete patterns. Inspired by these ideas, roboticists try to implement such building blocks using different techniques. In this paper, we show how to build such module by using a recurrent neural network (RNN) to encapsulate both discrete and rhythmical motion patterns into a single network. We evaluate the proposed system on a planar robotic manipulator. For training, we record several handwriting motions by back driving the robot manipulator. Finally, we demonstrate the ability to learn multiple motions (even discrete and rhythmic) and evaluate the pattern generation robustness in the presence of perturbations.
Institution: Ghent University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: European Commission, Vlaamse regering
OpenAlex ID: W2293707178