# Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction

arXiv: https://arxiv.org/abs/2104.01558 · HTML: https://mlsanity.com/paper/2104.01558/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 57 references that are in the map.
- First posted on arXiv 4 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [A Joint Speaker-Listener-Reinforcer Model for Referring Expressions](https://mlsanity.com/paper/1612.09542/) (2016)
- [AI Challenges in Human-Robot Cognitive Teaming](https://mlsanity.com/paper/1707.04775/) (2017)
- [Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments](https://mlsanity.com/paper/1904.07165/) (2019)
- [Toward Forgetting-Sensitive Referring Expression Generationfor Integrated Robot Architectures](https://mlsanity.com/paper/2007.08672/) (2020)

## Built on by (most cited first, 2 of 2)

- [Point What You Mean: Visually Grounded Instruction Policy](https://mlsanity.com/paper/2512.18933/) (2025)
- [Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters](https://mlsanity.com/paper/2604.15339/) (2026)

## Abstract (arXiv)

Intelligent robots designed to interact with humans in real scenarios need to be able to refer to entities actively by natural language. In spatial referring expression generation, the ambiguity is unavoidable due to the diversity of reference frames, which will lead to an understanding gap between humans and robots. To narrow this gap, in this paper, we propose a novel perspective-corrected spatial referring expression generation (PcSREG) approach for human-robot interaction by considering the selection of reference frames. The task of referring expression generation is simplified into the process of generating diverse spatial relation units. First, we pick out all landmarks in these spatial relation units according to the entropy of preference and allow its updating through a stack model. Then all possible referring expressions are generated according to different reference frame strategies. Finally, we evaluate every expression using a probabilistic referring expression resolution model and find the best expression that satisfies both of the appropriateness and effectiveness. We implement the proposed approach on a robot system and empirical experiments show that our approach can generate more effective spatial referring expressions for practical applications.