Multi-modal scene graph inspired policy for visual navigation

Abstract

Abstract Visual navigation needs the agent locate the given target with visual perception. To enable robots to effectively execute tasks, combining large language models (LLMs) with multi-modal inputs in navigation is necessary. While LLMs offer rich semantic knowledge, they lack specific real-world information and real-time interaction capabilities. This paper introduces a Multi-modal Scene Graph (MMSG) navigation framework that aligns LLMs with visual perception models to predict next steps. Firstly, a multi-modal scene dataset is constructed, containing triplets of object-relations-target words. We provide target words and lists of existing objects in the scene to generate a large number of instructions and corresponding action plans for GPT $$-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>-</mml:mo> </mml:math> 3.5. The generated data is then utilized for pre-train LLM for path planning. During inference, we discover objects in the scene by extending the DETR visual object detector to multi-view RGB image collected from different reachable positions. Experimental results show that path planning generated from MMSG outperforms state-of-the-art methods, indicating its feasibility in complex environments. We evaluate our methods on the ProTHOR dataset and show superior navigation performance.