Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/51610

TitleSkill memory in biped locomotion: using perceptual information to predict task outcome
Author(s)André, João
Santos, Cristina
Costa, Lino
KeywordsReinforcement learning
Bio-inspired
Skill memory
Issue dateJun-2016
PublisherSpringer
JournalJournal of Intelligent & Robotic Systems
Abstract(s)Robots must be able to adapt their motor behavior to unexpected situations in order to safely move among humans. A necessary step is to be able to predict failures, which result in behavior abnormalities and may cause irrecoverable damage to the robot and its surroundings, i.e. humans. In this paper we build a predictive model of sensor traces that enables early failure detection by means of a skill memory. Specifically, we propose an architecture based on a biped locomotion solution with improved robustness due to sensory feedback, and extend the concept of Associative Skill Memories (ASM) to periodic movements by introducing several mechanisms into the training workflow, such as linear interpolation and regression into a Dynamical Motion Primitive (DMP) system such that representation becomes time invariant and easily parameterizable. The failure detection mechanism applies statistical tests to determine the optimal operating conditions. Both training and failure testing were conducted on a DARwIn-OP inside a simulation environment to assess and validate the failure detection system proposed. Results show that the system performance in terms of the compromise between sensitivity and specificity is similar with and without the proposed mechanism, while achieving a significant data size reduction due to the periodic approach taken.
TypeArticle
URIhttp://hdl.handle.net/1822/51610
DOI10.1007/s10846-015-0197-z
ISSN0921-0296
e-ISSN1573-0409
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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