Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments
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Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We evaluate our method in ambiguous environments (e.g., environments that include very similar objects with similar relationships) relative to a state-of-the-art algorithm. We show that our method generates referring expressions that people find to be more accurate (∼30
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