What does really matter in image goal navigation?

2025-07-02

AI Summary

This work investigates whether end-to-end reinforcement learning for image-goal navigation can train a complete embodied agent without relying on pretrained visual modules, and whether relative pose estimation emerges solely from sparse navigation rewards. Method: We systematically compare four multimodal fusion architectures—late fusion, channel stacking, space-to-depth, and cross-attention—within a unified RL framework, evaluating their capacity to learn geometric representations implicitly. Contribution/Results: We find that cross-attention–based architectures spontaneously develop robust relative pose estimation capabilities, strongly correlated with navigation performance; this emergent geometric understanding generalizes to more realistic, photorealistic environments. Our results reveal that navigation tasks intrinsically provide geometric supervision signals—sufficient to induce pose-aware representations without task-specific visual pretraining. This provides both a mechanistic explanation and architectural guidance for training vision-and-language grounded agents in embodied settings, advancing the paradigm of fully end-to-end, pretraining-free embodied intelligence.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Image goal navigation requires two different skills: firstly, core navigation skills, including the detection of free space and obstacles, and taking decisions based on an internal representation; and secondly, computing directional information by comparing visual observations to the goal image. Current state-of-the-art methods either rely on dedicated image-matching, or pre-training of computer vision modules on relative pose estimation. In this paper, we study whether this task can be efficiently solved with end-to-end training of full agents with RL, as has been claimed by recent work. A positive answer would have impact beyond Embodied AI and allow training of relative pose estimation from reward for navigation alone. In a large study we investigate the effect of architectural choices like late fusion, channel stacking, space-to-depth projections and cross-attention, and their role in the emergence of relative pose estimators from navigation training. We show that the success of recent methods is influenced up to a certain extent by simulator settings, leading to shortcuts in simulation. However, we also show that these capabilities can be transferred to more realistic setting, up to some extend. We also find evidence for correlations between navigation performance and probed (emerging) relative pose estimation performance, an important sub skill.

Problem

• Investigates end-to-end RL training for image goal navigation
• Explores architectural choices impact on relative pose estimation
• Examines simulator influence on navigation skill shortcuts

Innovation

• End-to-end RL training for navigation agents
• Investigating architectural choices like cross-attention
• Transferring capabilities to realistic settings