Imitating by Generating: Deep Generative Models for Imitation of Interactive Tasks
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2020-04-16
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Language
en
Pages
14
Series
Frontiers in Robotics and AI, Volume 7
Abstract
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitation learning and active engagement with a skilled partner. They require the ability to predict and adapt to one's partner during an interaction. In this work we want to explore these ideas in a human-robot interaction setting in which a robot is required to learn interactive tasks from a combination of observational and kinesthetic learning. To this end, we propose a deep learning framework consisting of a number of components for (1) human and robot motion embedding, (2) motion prediction of the human partner, and (3) generation of robot joint trajectories matching the human motion. As long-term motion prediction methods often suffer from the problem of regression to the mean, our technical contribution here is a novel probabilistic latent variable model which does not predict in joint space but in latent space. To test the proposed method, we collect human-human interaction data and human-robot interaction data of four interactive tasks “hand-shake,” “hand-wave,” “parachute fist-bump,” and “rocket fist-bump.” We demonstrate experimentally the importance of predictive and adaptive components as well as low-level abstractions to successfully learn to imitate human behavior in interactive social tasks.Description
Keywords
Deep learning, Generative models, Human-robot interaction, Imitation learning, Sensorimotor coordination, Variational autoencoders
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Citation
Bütepage, J, Ghadirzadeh, A, Öztimur Karadaǧ, Ö, Björkman, M & Kragic, D 2020, ' Imitating by Generating : Deep Generative Models for Imitation of Interactive Tasks ', Frontiers in Robotics and AI, vol. 7, 47 . https://doi.org/10.3389/frobt.2020.00047