Predicting the internal model of a robotic system from its morphology

TitlePredicting the internal model of a robotic system from its morphology
Publication TypeJournal Article
Year of Publication2018
AuthorsDuran, AJ, del Pobil, AP
JournalRobotics and Autonomous Systems
Start Page33
Pagination33 - 43
Date Published12/2018
ISBN Number0921-8890
KeywordsInternal model, Model learning, Morphology, Neural networks, Visual learning

The estimation of the internal model of a robotic system results from the interaction of its morphology, sensors and actuators, with a particular environment. Model learning techniques, based on supervised machine learning, are widespread for determining the internal model. An important limitation of such approaches is that once a model has been learnt, it does not behave properly when the robot morphology is changed. From this it follows that there must exist a relationship between them. We propose a model for this correlation between the morphology and the internal model parameters, so that a new internal model can be predicted when the morphological parameters are modified. Different neural network architectures are proposed to address this high dimensional regression problem. A case study is analyzed in detail to illustrate and evaluate the performance of the approach, namely, a pan–tilt robot head executing saccadic movements. The best results are obtained for an architecture with parallel neural networks. Our results can be instrumental in state-of-the-art trends such as self-reconfigurable robots, reproducible research, cyber–physical robotic systems or cloud robotics, in which internal models would available as shared knowledge, so that robots with different morphologies can readily exhibit a particular behavior in a given environment.

Short TitleRobotics and Autonomous Systems