soltoggioLemmeSteilROBIO2012Preprint.pdf (1.97 MB)
Download file

Using movement primitives in interpreting and decomposing complex trajectories in learning-by-doing

Download (1.97 MB)
conference contribution
posted on 15.10.2014, 10:05 authored by Andrea SoltoggioAndrea Soltoggio, Andre Lemme, Jochen Steil
Learning and reproducing complex movements is an important skill for robots. However, while humans can learn and generalise new complex trajectories, robots are often programmed to execute point-by-point precise but fixed patterns. This study proposes a method for decomposing new complex trajectories into a set of known robot-based primitives. Instead of reproducing accurately an observed trajectory, the robot interprets it as a composition of its own previously acquired primitive movements. The method attempts initially a rough approximation with the idea of capturing the most essential features of the movement. Observing the discrepancy between the demonstrated and reproduced trajectories, the process then proceeds with incremental decompositions. The method is tested on both geometric and human generated trajectories. The shift from a data-centred view to an agent-centred view in learning trajectories results in generalisation properties like the abstraction to primitives and noise suppression. This study suggests a novel approach to learning complex robot motor patterns that builds upon existing motor skills. Applications include drawing, writing, movement generation and object manipulation in a variety of tasks.

Funding

European Community’s Seventh Framework Programm FP7/2007- 2013, Challenge 2, Cognitive Systems, Interaction, Robotics under grant agreement No 248311 - AMARSi

History

School

  • Science

Department

  • Computer Science

Published in

2012 IEEE International Conference on Robotics and Biomimetics, ROBIO 2012 - Conference Digest

Pages

1427 - 1433

Citation

SOLTOGGIO, A., LEMME, A. and STEIL, J., 2012. Using movement primitives in interpreting and decomposing complex trajectories in learning-by-doing. IN: ROBIO 2012: IEEE International Conference on Robotics and Biomimetics, 11-14 December 2012, pp. 1427 - 1433.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2012

Notes

This is a conference paper © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

978-1-4673-2125-9

Language

en