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Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany.ORCID iD: 0000-0003-0129-0540
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany.ORCID iD: 0000-0002-0816-2042
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany.ORCID iD: 0000-0002-2540-1869
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2018 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, no 4, p. 4007-4014Article in journal (Refereed) Published
Abstract [en]

We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory that facilitates encoding, recalling, and predicting action experiences. Our proposed unsupervised deep episodic memory model as follows: First, encodes observed actions in a latent vector space and, based on this latent encoding, second, infers most similar episodes previously experienced, third, reconstructs original episodes, and finally, predicts future frames in an end-to-end fashion. Results show that conceptually similar actions are mapped into the same region of the latent vector space. Based on these results, we introduce an action matching and retrieval mechanism, benchmark its performance on two large-scale action datasets, 20BN-something-something and ActivityNet and evaluate its generalization capability in a real-world scenario on a humanoid robot.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2018. Vol. 3, no 4, p. 4007-4014
Keywords [en]
Learning and adaptive systems, visual learning, deep learning in robotics and automation
National Category
Robotics
Identifiers
URN: urn:nbn:se:hh:diva-38426DOI: 10.1109/LRA.2018.2860057ISI: 000441935900003OAI: oai:DiVA.org:hh-38426DiVA, id: diva2:1266021
Funder
EU, Horizon 2020, 641100 (TimeStorm)German Research Foundation (DFG), SPP 1527Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2018-11-27Bibliographically approved

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Aksoy, Eren

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2324252627282926 of 54
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