Towards Intelligent Social Robots: Current Advances in Cognitive Robotics Proceedings of the Full Day Workshop Towards Intelligent Social Robots: Current Advances in Cognitive Robotics in Conjunction with Humanoids 2015 Towards Emerging Multimodal Cognitive Representations from Neural Self-Organization release_rev_174a0ce8-27fa-4a26-bd92-9bb6a1b9c24a

Abstract

The integration of multisensory information plays a crucial role in autonomous robotics. In this work, we investigate how robust multimodal representations can naturally develop in a self-organized manner from co-occurring multisensory inputs. We propose a hierarchical learning architecture with growing self-organizing neural networks for learning human actions from audiovisual inputs. Associative links between unimodal representations are incrementally learned by a semi-supervised algorithm with bidirectional connectivity that takes into account inherent spatiotemporal dynamics of the input. Experiments on a dataset of 10 full-body actions show that our architecture is able to learn action-word mappings without the need of segmenting training samples for ground-truth labelling. Instead, multimodal representations of actions are obtained using the co-activation of action features from video sequences and labels from automatic speech recognition. Promising experimental results encourage the extension of our architecture in several directions.
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