Contextual Affordances for Action-Effect Prediction in a Robotic-Cleaning Task

Francisco Cruz , German I. Parisi , Stefan Wermter

IEEE/RSJ International Conference on Intelligent Robots and Systems, Workshop Learning Object Affordances: A Fundamental Step to Allow Prediction, Planning and Tool Use?, - Oct 2015

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Affordances are a useful method to anticipate the effect of an action performed by an agent. In this work, we present a robotic-cleaning task using contextual affordances implemented through a self-organizing neural network to predict the effect of the performed actions and avoid failed states. Current results on a simulated robot environment show that our architecture is able to predict future states with high accuracy.

@InProceedings{CPW15, 
 	 author =  {Cruz, Francisco and Parisi, German I. and Wermter, Stefan},  
 	 title = {Contextual Affordances for Action-Effect Prediction in a Robotic-Cleaning Task}, 
 	 booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, Workshop Learning Object Affordances: A Fundamental Step to Allow Prediction, Planning and Tool Use?},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {},
 	 year = {2015},
 	 month = {Oct},
 	 publisher = {},
 	 doi = {}, 
 }