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Grounding of human environments and activities for autonomous robots
KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.
Vise andre og tillknytning
2017 (engelsk)Inngår i: IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence , 2017, s. 1395-1402Konferansepaper (Fagfellevurdert)
Abstract [en]

With the recent proliferation of human-oriented robotic applications in domestic and industrial scenarios, it is vital for robots to continually learn about their environments and about the humans they share their environments with. In this paper, we present a novel, online, incremental framework for unsupervised symbol grounding in real-world, human environments for autonomous robots. We demonstrate the flexibility of the framework by learning about colours, people names, usable objects and simple human activities, integrating stateofthe-art object segmentation, pose estimation, activity analysis along with a number of sensory input encodings into a continual learning framework. Natural language is grounded to the learned concepts, enabling the robot to communicate in a human-understandable way. We show, using a challenging real-world dataset of human activities as perceived by a mobile robot, that our framework is able to extract useful concepts, ground natural language descriptions to them, and, as a proof-ofconcept, generate simple sentences from templates to describe people and the activities they are engaged in.

sted, utgiver, år, opplag, sider
International Joint Conferences on Artificial Intelligence , 2017. s. 1395-1402
Serie
IJCAI International Joint Conference on Artificial Intelligence, ISSN 1045-0823
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-217131Scopus ID: 2-s2.0-85031897942ISBN: 9780999241103 (tryckt)OAI: oai:DiVA.org:kth-217131DiVA, id: diva2:1154078
Konferanse
26th International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19 August 2017 through 25 August 2017
Merknad

QC 20171101

Tilgjengelig fra: 2017-11-01 Laget: 2017-11-01 Sist oppdatert: 2017-11-01bibliografisk kontrollert

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Totalt: 662 treff
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