Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism

WS 2020 · Patricio Cerda-Mardini, Vladimir Araujo, Alvaro Soto ·

We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navigation. We follow the framework established by (Zang et al., 2018a) that proposes the use of a navigation graph as a knowledge base for the task. Our results show significant performance gains when translating instructions on previously unseen environments, therefore, improving the generalization capabilities of the model.

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Robot Navigation

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Linear Layer • Multi-Head Attention • Scaled Dot-Product Attention • Softmax

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