# Language, Environment, and Robotic Navigation

arXiv: https://arxiv.org/abs/2404.03049 · HTML: https://mlsanity.com/paper/2404.03049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Visual Language Maps for Robot Navigation](https://mlsanity.com/paper/2210.05714/) (2022)
- [Interactive Language: Talking to Robots in Real Time](https://mlsanity.com/paper/2210.06407/) (2022)
- [Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation](https://mlsanity.com/paper/2109.01115/) (2021)
- [Navigating to Objects in the Real World](https://mlsanity.com/paper/2212.00922/) (2022)
- [Grounded Language Learning in a Simulated 3D World](https://mlsanity.com/paper/1706.06551/) (2017)
- [PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards](https://mlsanity.com/paper/2007.15543/) (2020)
- [Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data](https://mlsanity.com/paper/2305.19075/) (2023)
- [Meta-Reinforcement Learning via Language Instructions](https://mlsanity.com/paper/2209.04924/) (2022)
- [Zipfian environments for Reinforcement Learning](https://mlsanity.com/paper/2203.08222/) (2022)
- [Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results](https://mlsanity.com/paper/2212.06759/) (2022)
- [Grounding and Distinguishing Conceptual Vocabulary Through Similarity Learning in Embodied Simulations](https://mlsanity.com/paper/2305.13668/) (2023)
- [Reconstructing Maps from Text](https://mlsanity.com/paper/2005.08932/) (2020)
- [Semantic Navigation Using Building Information on Construction Sites](https://mlsanity.com/paper/2104.10296/) (2021)

## Abstract (arXiv)

This paper explores the integration of linguistic inputs within robotic navigation systems, drawing upon the symbol interdependency hypothesis to bridge the divide between symbolic and embodied cognition. It examines previous work incorporating language and semantics into Neural Network (NN) and Simultaneous Localization and Mapping (SLAM) approaches, highlighting how these integrations have advanced the field. By contrasting abstract symbol manipulation with sensory-motor grounding, we propose a unified framework where language functions both as an abstract communicative system and as a grounded representation of perceptual experiences. Our review of cognitive models of distributional semantics and their application to autonomous agents underscores the transformative potential of language-integrated systems.