Slow feature analysis: unsupervised learning of invariances
Neural Computation
Slow feature analysis: a theoretical analysis of optimal free responses
Neural Computation
Connection Science - Language and Robots
How experience of the body shapes language about space
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Modeling, stability and control of biped robots-a general framework
Automatica (Journal of IFAC)
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This paper presents a biologically inspired approach to posture recognition and posture change detection for a biped robot. Slow Feature Analysis, an algorithm developed by theoretical biologists for extracting slowly changing signals from signals varying on a fast time scale, is applied to the problem of recognizing the posture of biped humanoid robots over time and successively on the recognition of the change of posture. Both the recognition of basic static postures, like lying and standing, of peer robots via visual sensory information and the recognition of the same postures via internal proprioceptive sensors are considered. Given promising results in this domain we extend the application of the method onto the dynamic domain of detecting the change of posture, specifically we show the utility of the algorithm for detecting when a robot falls.