In this paper we rely upon the concept of Spatio-Temporal Affordances (STA) to formalize the objective function to learn affordance descriptors. Such a function allows to better encode action semantics related to the environment. We qualitatively evaluate obtained results over the learned spatial model for two different tasks.
Using Spatio-Temporal Affordances to Represent Robot Action Semantics / Riccio, Francesco; Capobianco, Roberto; Nardi, Daniele. - (2016), pp. -------. ( Machine Learning Methods for High-Level Cognitive Capabilities in Robotics (Workshop at IROS) Daejeon, South Korea 14 October 2016).
Using Spatio-Temporal Affordances to Represent Robot Action Semantics
RICCIO, FRANCESCO;CAPOBIANCO, ROBERTO;NARDI, Daniele
2016
Abstract
In this paper we rely upon the concept of Spatio-Temporal Affordances (STA) to formalize the objective function to learn affordance descriptors. Such a function allows to better encode action semantics related to the environment. We qualitatively evaluate obtained results over the learned spatial model for two different tasks.File allegati a questo prodotto
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