# Open Scene Graphs for Open World Object-Goal Navigation

arXiv: https://arxiv.org/abs/2407.02473 · HTML: https://mlsanity.com/paper/2407.02473/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [ImagineNav: Prompting Vision-Language Models as Embodied Navigator through Scene Imagination](https://mlsanity.com/paper/2410.09874/) (2024)

## Abstract (arXiv)

How can we build robots for open-world semantic navigation tasks, like searching for target objects in novel scenes? While foundation models have the rich knowledge and generalisation needed for these tasks, a suitable scene representation is needed to connect them into a complete robot system. We address this with Open Scene Graphs (OSGs), a topo-semantic representation that retains and organises open-set scene information for these models, and has a structure that can be configured for different environment types. We integrate foundation models and OSGs into the OpenSearch system for Open World Object-Goal Navigation, which is capable of searching for open-set objects specified in natural language, while generalising zero-shot across diverse environments and embodiments. Our OSGs enhance reasoning with Large Language Models (LLM), enabling robust object-goal navigation outperforming existing LLM approaches. Through simulation and real-world experiments, we validate OpenSearch's generalisation across varied environments, robots and novel instructions.