Reasoning in visual navigation of end-to-end trained agents: a dynamical systems approach

2025-03-11

AI Summary

This work investigates the inference mechanisms of end-to-end trained visual navigation agents deployed on real-world high-speed mobile robots. Addressing core challenges in open-loop prediction—including dynamics modeling, perception-action coupling, scene-structural memory, and finite-horizon planning—we propose a dynamical-systems-inspired analytical framework. It integrates latent-state probing, posterior value-function visualization, and open-loop trajectory evaluation, systematically disentangling internal representations across thousands of physical experiments. Our key findings are: (1) latent states encode interpretable short-term motion plans; (2) the value function explicitly encodes long-range goal semantics; and (3) the agent inherently performs dynamic modeling, memory-guided decision-making, and goal-directed planning. These results establish a novel paradigm for explainable modeling and robust control in embodied intelligence, bridging representation learning with real-world robotic autonomy.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Visual Reasoning & Symbolic Representations
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Progress in Embodied AI has made it possible for end-to-end-trained agents to navigate in photo-realistic environments with high-level reasoning and zero-shot or language-conditioned behavior, but benchmarks are still dominated by simulation. In this work, we focus on the fine-grained behavior of fast-moving real robots and present a large-scale experimental study involving umepisodes{} navigation episodes in a real environment with a physical robot, where we analyze the type of reasoning emerging from end-to-end training. In particular, we study the presence of realistic dynamics which the agent learned for open-loop forecasting, and their interplay with sensing. We analyze the way the agent uses latent memory to hold elements of the scene structure and information gathered during exploration. We probe the planning capabilities of the agent, and find in its memory evidence for somewhat precise plans over a limited horizon. Furthermore, we show in a post-hoc analysis that the value function learned by the agent relates to long-term planning. Put together, our experiments paint a new picture on how using tools from computer vision and sequential decision making have led to new capabilities in robotics and control. An interactive tool is available at europe.naverlabs.com/research/publications/reasoning-in-visual-navigation-of-end-to-end-trained-agents.

Problem

• Analyzing reasoning in end-to-end-trained agents for real-world robot navigation.
• Studying the interplay between learned dynamics and sensing in navigation.
• Exploring latent memory usage and planning capabilities in trained agents.

Innovation

• End-to-end training for real robot navigation
• Latent memory for scene structure retention
• Value function linked to long-term planning