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Movement primitives as a robotic tool to interpret trajectories through learning-by-doing

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journal contribution
posted on 2015-03-13, 10:22 authored by Andrea SoltoggioAndrea Soltoggio, Andre Lemme
Articulated movements are fundamental in many human and robotic tasks. While humans can learn and generalise arbitrarily long sequences of movements, and particularly can optimise them to fit the constraints and features of their body, robots are often programmed to execute point-to-point precise but fixed patterns. This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives. Instead of achieving accurate reproductions, the proposed approach aims at interpreting data in an agent-centred fashion, according to an agent's primitive movements. The method improves the accuracy of a reproduction with an incremental process that seeks first a rough approximation by capturing the most essential features of a demonstrated trajectory. Observing the discrepancy between the demonstrated and reproduced trajectories, the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory. The aim is to achieve an agent-centred interpretation and progressive learning that fits in the first place the robots' capability, as opposed to a data-centred decomposition analysis. Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method. In particular, because trajectories are understood and abstracted by means of agent-optimised primitives, the method has two main features: 1) Reproduced trajectories are general and represent an abstraction of the data. 2) The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection. This study suggests a novel bio-inspired approach to interpreting, learning and reproducing articulated movements and trajectories. Possible applications include drawing, writing, movement generation, object manipulation, and other tasks where the performance requires human-like interpretation and generalisation capabilities.

Funding

This work was supported by the European Community's Seventh Framework Programme FP7/2007-2013, Challenge 2, Cognitive Systems, Interaction, Robotics (Grant number: 248311-AMARSi).

History

School

  • Science

Department

  • Computer Science

Published in

International Journal of Automation and Computing

Volume

10

Issue

5

Pages

375 - 386

Citation

SOLTOGGIO, A. and LEMME, A., 2013. Movement primitives as a robotic tool to interpret trajectories through learning-by-doing. International Journal of Automation and Computing, 10 (5), pp. 375-386.

Publisher

© Springer

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

2013

Notes

The final publication is available at Springer via http://dx.doi.org/10.1007/s11633-013-0734-9

ISSN

1476-8186

eISSN

1751-8520

Language

  • en