Open Scene Graphs for Open World Object-Goal Navigation

TL;DR

This paper introduces Open Scene Graphs, a topo-semantic representation that, combined with foundation models, enables robots to perform open-world object-goal navigation with zero-shot generalization in diverse environments.

Contribution

It proposes Open Scene Graphs as a novel scene representation that integrates with foundation models for improved open-world navigation tasks.

Findings

OSGs improve reasoning with Large Language Models.

OpenSearch system achieves zero-shot generalization.

Robust object-goal navigation demonstrated in simulations and real-world experiments.

Abstract

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.