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Imitating by Generating: Deep Generative Models for Imitation of Interactive Tasks.
Bütepage, Judith; Ghadirzadeh, Ali; Öztimur Karadaǧ, Özge; Björkman, Mårten; Kragic, Danica.
Afiliação
  • Bütepage J; Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Ghadirzadeh A; Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Öztimur Karadaǧ Ö; Intelligent Robotics Research Group, Aalto University, Espoo, Finland.
  • Björkman M; Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Kragic D; Department of Computer Engineering, Alanya Alaaddin Keykubat University, Antalya, Turkey.
Front Robot AI ; 7: 47, 2020.
Article em En | MEDLINE | ID: mdl-33501215
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.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article