Speed Adaptation in Learning from Demonstration through Latent Space Formulation

Abstract

SUMMARYPerforming 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.

Time-Aware Multi-Agent Symbiosis

“…Beyond improvements on the three core modules, future work regards the exploitation of existing computational models of human time perception (Maniadakis and Trahanias, 2012 , 2016b ), the time-informed kinesthetic teaching of robots (Koskinopoulou et al, 2018 , 2019 ), and how sense of time interacts with emotions, a rather unexplored direction that has the potential to significantly improve sHRI (Maniadakis et al, 2017 ).…”

Special Issue on Human–Robot Interaction (HRI)

“…An imitation framework by observation for the speed adaptation of a robot by demonstrated actions of arm motions is presented by Koskinopoulou et al 8 The described framework has been employed in the execution of a realistic service scenario. An optimal controller based on manipulability for master-slave robotic systems is presented by Torabi et al 9 The performance of the controller is evaluated via a comparison with two other existing control strategies.…”