Language for learning complex human-object interactions
2013 (English)In: 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE Computer Society, 2013, 4997-5002 p.Conference paper (Refereed)
In this paper we use a Hierarchical Hidden Markov Model (HHMM) to represent and learn complex activities/task performed by humans/robots in everyday life. Action primitives are used as a grammar to represent complex human behaviour and learn the interactions and behaviour of human/robots with different objects. The main contribution is the use of a probabilistic model capable of representing behaviours at multiple levels of abstraction to support the proposed hypothesis. The hierarchical nature of the model allows decomposition of the complex task into simple action primitives. The framework is evaluated with data collected for tasks of everyday importance performed by a human user.
Place, publisher, year, edition, pages
IEEE Computer Society, 2013. 4997-5002 p.
, Proceedings - IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Complex activity, Complex task, Hierarchical hidden markov models, Human behaviours, Human users, Human-object interaction, Multiple levels, Probabilistic modeling, Robotics
IdentifiersURN: urn:nbn:se:kth:diva-139938DOI: 10.1109/ICRA.2013.6631291ScopusID: 2-s2.0-84887282395ISBN: 978-146735641-1OAI: oai:DiVA.org:kth-139938DiVA: diva2:687831
2013 IEEE International Conference on Robotics and Automation, ICRA 2013; Karlsruhe; Germany; 6 May 2013 through 10 May 2013
QC 201401152014-01-152014-01-152014-01-15Bibliographically approved