Learning Modular Policy for Multi-Floor Object Navigation:A Factorized Framework for Diagnostic Study

Abstract:Object-goal navigation (ObjectNav) in multi-floor scenarios presents a challenge due to sparse rewards caused by long-horizon decision-making. In this paper, we propose a diagnostic study based on a modular framework with an effective learnable policy to analyze failure factors in multi-floor scenarios. To achieve an effective policy for diagnosis, we design the hierarchical factorization policy that deconstructs a single global policy into an intra-floor exploration policy and an inter-floor switching policy. To providing an effective initialization for Reinforcement Learning (RL), the lightweight intra-floor policy is learned by distilling the exploration logic of Visual Language Models (VLMs). Under idealized assumptions, we show that the factorized policy is theoretically equivalent to a single global policy at the policy-representation level. Experiment results indicate that perception performance and stair climbing stability are the primary bottlenecks in multi-floor navigation.

Robotics (cs.RO)

arXiv:2610.06958 [cs.RO]

(or arXiv:2610.06958v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.06958 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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