Learning human arm movements by imitation: Evaluation of a biologically inspired connectionist architecture
This paper evaluates a model of human imitation of abstract, two-arm movements. The model consists of a hierarchy of artificial neural networks, which are abstractions of brain regions involved in visuo-motor control. The model is validated in a biomechanical simulation of a 37 degree of freedom (DOF) humanoid. Input to the model are data from human arm movements recorded using video and marker-based tracking systems. Results show a high qualitative and quantitative agreement with human data. The model's reproduction is better or comparable to that of human subjects imitating the same movements. (C) 2001 Elsevier Science B.V. AII rights reserved.
learning imitation artificial neural networks