Long-term knowledge acquisition in a memory-based epigenetic robot architecture for verbal interaction

Ferdian Pratama, Fulvio Mastrogiovanni, Sungmoon Jeong, Nak Young Chong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We present a robot cognitive framework based on (a) a memory-like architecture; and (b) the notion of 'context'. We posit that relying solely on machine learning techniques may not be the right approach for a long-term, continuous knowledge acquisition. Since we are interested in long-term human-robot interaction, we focus on a scenario where a robot 'remembers' relevant events happening in the environment. By visually sensing its surroundings, the robot is expected to infer and remember snapshots of events, and recall specific past events based on inputs and contextual information from humans. Using a COTS vision frameworks for the experiment, we show that the robot is able to form 'memories' and recall related events based on cues and the context given during the human-robot interaction process.

Original languageEnglish
Title of host publicationRO-MAN 2015 - 24th IEEE International Symposium on Robot and Human Interactive Communication, Symposium Digest
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-30
Number of pages6
ISBN (Electronic)9781467367042
DOIs
StatePublished - 20 Nov 2015
Event24th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2015 - Kobe, Japan
Duration: 31 Aug 20154 Sep 2015

Publication series

NameProceedings - IEEE International Workshop on Robot and Human Interactive Communication
Volume2015-November

Conference

Conference24th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2015
Country/TerritoryJapan
CityKobe
Period31/08/154/09/15

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