RANa: Retrieval-Augmented Navigation

• URL: http://arxiv.org/abs/2504.03524v1
• Date: Fri, 04 Apr 2025 15:22:02 GMT
• Title: RANa: Retrieval-Augmented Navigation
• Authors: Gianluca Monaci, Rafael S. Rezende, Romain Deffayet, Gabriela Csurka, Guillaume Bono, Hervé Déjean, Stéphane Clinchant, Christian Wolf,
• Abstract summary: We introduce a new retrieval-augmented agent, trained with RL, capable of querying a database collected from previous episodes in the same environment.<n>We show that retrieval allows zero-shot transfer across tasks and environments while significantly improving performance.
• Score: 18.651051807288013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Methods for navigation based on large-scale learning typically treat each episode as a new problem, where the agent is spawned with a clean memory in an unknown environment. While these generalization capabilities to an unknown environment are extremely important, we claim that, in a realistic setting, an agent should have the capacity of exploiting information collected during earlier robot operations. We address this by introducing a new retrieval-augmented agent, trained with RL, capable of querying a database collected from previous episodes in the same environment and learning how to integrate this additional context information. We introduce a unique agent architecture for the general navigation task, evaluated on ObjectNav, ImageNav and Instance-ImageNav. Our retrieval and context encoding methods are data-driven and heavily employ vision foundation models (FM) for both semantic and geometric understanding. We propose new benchmarks for these settings and we show that retrieval allows zero-shot transfer across tasks and environments while significantly improving performance.

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