Imitating by Generating: Deep Generative Models for Imitation of Interactive Tasks

Judith Bütepage*, Ali Ghadirzadeh, Özge Öztimur Karadaǧ, Mårten Björkman, Danica Kragic

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

77 Lataukset (Pure)


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.

JulkaisuFrontiers in Robotics and AI
DOI - pysyväislinkit
TilaJulkaistu - 16 huhtikuuta 2020
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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    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, 7, [47].