Multi-Floor Zero-Shot Object Navigation Policy

TL;DR

This paper introduces a novel Multi-floor Navigation Policy (MFNP) that enables robots to navigate across multiple floors in complex environments using advanced reasoning and exploration strategies, significantly improving success rates in zero-shot object navigation tasks.

Contribution

The paper presents the first comprehensive multi-floor navigation policy integrating multi-modal reasoning and inter-floor transition strategies for zero-shot object navigation.

Findings

MFNP outperforms existing methods in success rate and exploration efficiency.

Ablation studies confirm the importance of each component in MFNP.

Real-world experiments demonstrate successful multi-floor navigation by a quadruped robot.

Abstract

Object navigation in multi-floor environments presents a formidable challenge in robotics, requiring sophisticated spatial reasoning and adaptive exploration strategies. Traditional approaches have primarily focused on single-floor scenarios, overlooking the complexities introduced by multi-floor structures. To address these challenges, we first propose a Multi-floor Navigation Policy (MFNP) and implement it in Zero-Shot object navigation tasks. Our framework comprises three key components: (i) Multi-floor Navigation Policy, which enables an agent to explore across multiple floors; (ii) Multi-modal Large Language Models (MLLMs) for reasoning in the navigation process; and (iii) Inter-Floor Navigation, ensuring efficient floor transitions. We evaluate MFNP on the Habitat-Matterport 3D (HM3D) and Matterport 3D (MP3D) datasets, both include multi-floor scenes. Our experiment results demonstrate that MFNP significantly outperforms all the existing methods in Zero-Shot object navigation, achieving higher success rates and improved exploration efficiency. Ablation studies further highlight the effectiveness of each component in addressing the unique challenges of multi-floor navigation. Meanwhile, we conducted real-world experiments to evaluate the feasibility of our policy. Upon deployment of MFNP, the Unitree quadruped robot demonstrated successful multi-floor navigation and found the target object in a completely unseen environment. By introducing MFNP, we offer a new paradigm for tackling complex, multi-floor environments in object navigation tasks, opening avenues for future research in visual-based navigation in realistic, multi-floor settings.