Robust and Interpretable Grounding of Spatial References with Relation Networks
This addresses the challenge of spatial understanding for autonomous navigation and robotic manipulation, with incremental improvements in robustness and interpretability.
The paper tackled the problem of learning robust and interpretable representations for spatial references in natural language, achieving a 17% improvement in predicting goal locations and a 15% improvement in robustness over state-of-the-art systems.
Learning representations of spatial references in natural language is a key challenge in tasks like autonomous navigation and robotic manipulation. Recent work has investigated various neural architectures for learning multi-modal representations for spatial concepts. However, the lack of explicit reasoning over entities makes such approaches vulnerable to noise in input text or state observations. In this paper, we develop effective models for understanding spatial references in text that are robust and interpretable, without sacrificing performance. We design a text-conditioned \textit{relation network} whose parameters are dynamically computed with a cross-modal attention module to capture fine-grained spatial relations between entities. This design choice provides interpretability of learned intermediate outputs. Experiments across three tasks demonstrate that our model achieves superior performance, with a 17\% improvement in predicting goal locations and a 15\% improvement in robustness compared to state-of-the-art systems.