Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/16985

Title: Movement primitives as a robotic tool to interpret trajectories through learning-by-doing
Authors: Soltoggio, Andrea
Lemme, Andre
Keywords: Learning
Movement primitives
Pattern matching
Trajectory decomposition
Issue Date: 2013
Publisher: © Springer
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.
Abstract: 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.
Description: The final publication is available at Springer via http://dx.doi.org/10.1007/s11633-013-0734-9
Sponsor: 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).
Version: Accepted for publication
DOI: 10.1007/s11633-013-0734-9
URI: https://dspace.lboro.ac.uk/2134/16985
Publisher Link: http://dx.doi.org/10.1007/s11633-013-0734-9
ISSN: 1476-8186
Appears in Collections:Published Articles (Computer Science)

Files associated with this item:

File Description SizeFormat
ijacSoltoggioLemme2013Preprint.pdfAccepted version747.67 kBAdobe PDFView/Open


SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.