Speed Adaptation in Learning from Demonstration through Latent Space Formulation

Maria Koskinopoulou*, Michail Maniadakis, Panos Trahanias

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1867-1879
Number of pages13
Issue number10
Publication statusPublished - Oct 2020


  • Artificial temporal cognition
  • Human-robot interaction
  • Latent representation
  • Learning from demonstration
  • Temporal planning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • General Mathematics
  • Modelling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Control and Optimization


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