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arXiv · 1909.07201

MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations

Oliver Struckmeier·Kshitij Tiwari·Shirin Dora·Martin J. Pearson·Sander M. Bohte·Cyriel MA Pennartz·Ville Kyrki
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Abstract

Extracting and binding salient information from different sensory modalities to determine common features in the environment is a significant challenge in robotics. Here we present MuPNet (Multi-modal Predictive Coding Network), a biologically plausible network architecture for extracting joint latent features from visuo-tactile sensory data gathered from a biomimetic mobile robot. In this study we evaluate MuPNet applied to place recognition as a simulated biomimetic robot platform explores visually aliased environments. The F1 scores demonstrate that its performance over prior hand-crafted sensory feature extraction techniques is equivalent under controlled conditions, with significant improvement when operating in novel environments.

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Oliver Struckmeier, Kshitij Tiwari, Shirin Dora, Martin J. Pearson, Sander M. Bohte, Cyriel MA Pennartz, Ville Kyrki. 2019-09-16. MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations. https://arxiv.org/abs/1909.07201

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