Abstract
In this paper, we develop a neural network model that learns representations of 3D objects via tactile exploration. The basic principle is that the hand is considered as an autonomous ‘navigating agent’, traveling within the ‘environment’ of a 3D object. We adapt a model of hippocampal place cells, which learns the structure of a 2D environment by exploiting constraints imposed by the environment’s boundaries on the agent’s movement, and perceptual information about landmarks in the environment. In the current paper, our focus is on 3D analogues of these 2D information sources. We systematically investigate the information about object geometry that is provided by navigation constraints in a simple cuboid, and by tactile landmarks. We find that an asymmetric cuboid conveys more information to the navigator than a symmetric cuboid (i.e., a cube) – and that landmarks convey additional information independently from asymmetry.
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Acknowledgment
The authors would like to thank Martin Takac for the earlier work on a SOM-based navigation model.
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Yan, X., Knott, A., Mills, S. (2018). A Model for Learning Representations of 3D Objects Through Tactile Exploration: Effects of Object Asymmetries and Landmarks. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_27
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DOI: https://doi.org/10.1007/978-3-030-03991-2_27
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