# Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction

arXiv: https://arxiv.org/abs/1604.06721 · HTML: https://mlsanity.com/paper/1604.06721/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2016.

## Built on by (most cited first, 3 of 3)

- [Beyond Text: Utilizing Vocal Cues to Improve Decision Making in LLMs for Robot Navigation Tasks](https://mlsanity.com/paper/2402.03494/) (2024)
- [Embodying Pre-Trained Word Embeddings Through Robot Actions](https://mlsanity.com/paper/2104.08521/) (2021)
- [Natural Language Instructions for Intuitive Human Interaction with Robotic Assistants in Field Construction Work](https://mlsanity.com/paper/2307.04195/) (2023)

## Abstract (arXiv)

We develop a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction Grammar (ECG) [Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference resolution problems and other issues related to deep semantics and compositionality of natural language. This also includes verbal interaction with humans to clarify commands and queries that are too ambiguous to be executed safely. We implement our NLU framework as a ROS package and present proof-of-concept scenarios with different robots, as well as a survey on the state of the art.