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Speed Adaptation in Learning from Demonstration through Latent Space Formulation

Published online by Cambridge University Press:  17 October 2019

Maria Koskinopoulou Open the ORCID record for Maria Koskinopoulou [Opens in a new window] ,
Michail Maniadakis  and
Panos Trahanias
Maria Koskinopoulou*
Affiliation:
Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece Department of Computer Science, University of Crete, Heraklion, Crete, Greece
Michail Maniadakis
Affiliation:
Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece
Panos Trahanias
Affiliation:
Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece Department of Computer Science, University of Crete, Heraklion, Crete, Greece
*
*Corresponding author. E-mail: [email protected]
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Summary

Performing actions in a timely manner is an indispensable aspect in everyday human activities. Accordingly, it has to be present in robotic systems if they are going to seamlessly interact with humans. The current work addresses the problem of learning both the spatial and temporal characteristics of human motions from observation. We formulate learning as a mapping between two worlds (the observed and the action ones). This mapping is realized via an abstract intermediate representation termed “Latent Space.” Learned actions can be subsequently invoked in the context of more complex human–robot interaction (HRI) scenarios. Unlike previous learning from demonstration (LfD) methods that cope only with the spatial features of an action, the formulated scheme effectively encompasses spatial and temporal aspects. Learned actions are reproduced under the high-level control of a time-informed task planner. During the implementation of the studied scenarios, temporal and physical constraints may impose speed adaptations in the reproduced actions. The employed latent space representation readily supports such variations, giving rise to novel actions in the temporal domain. Experimental results demonstrate the effectiveness of the proposed scheme in the implementation of HRI scenarios. Finally, a set of well-defined evaluation metrics are introduced to assess the validity of the proposed approach considering the temporal and spatial consistency of the reproduced behaviors.

Keywords

Human–robot interactionLearning from demonstrationArtificial temporal cognitionTemporal planningLatent representation
Type
Articles
Information
Robotica , Volume 38 , Special Issue 10: Human–Robot Interaction (HRI) , October 2020 , pp. 1867 - 1879
Copyright
© Cambridge University Press 2019

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Koskinopoulou et al. supplementary material

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