aiqbench analysis
AllDayNav demonstrates that implicit, memory-driven reinforcement learning can achieve near-perfect navigation success rates in dynamic environments without relying on explicit maps. By leveraging a self-evolving multimodal memory to encode scene dynamics directly into large model parameters, this framework offers a scalable alternative to traditional mapping methods for lifelong robot autonomy. The approach effectively bridges the gap between synthetic training and real-world deployment, showing robust generalization across cross-room and cross-task scenarios.
AllDayNav: Lifelong Navigation via Real-World Reinforcement Learning
arXiv:2606.10927v1 Announce Type: new Abstract: Lifelong embodied navigation in dynamic environments requires robots to form persistent scene understanding from fragmentary observations, which remains difficult for existing methods that rely on explicit maps or scene graphs and struggle to generalize beyond structured settings. We propose AllDayNav, a lifelong self-learning navigation framework that implicitly encodes scene dynamics into the billion-scale parameters of a large model via reinforcement learning, powered by a self-evolving multimodal memory that maintains and updates visual keyframes, semantic descriptions, and temporal context while autonomously generating open-vocabulary instructions, image goals, and structured rewards. Experiments in both synthetic and real-world environments across cross-room, cross-episode, and cross-task scenarios show that AllDayNav achieves success rates approaching $100\%$ and consistently surpasses strong map-based, VLM, and RL baselines in path efficiency and robustness, demonstrating implicit, memory-driven reinforcement learning as a scalable alternative to explicit mapping for reliable lifelong navigation.
Abstract:Lifelong embodied navigation in dynamic environments requires robots to form persistent scene understanding from fragmentary observations, which remains difficult for existing methods that rely on explicit maps or scene graphs and struggle to generalize beyond structured settings. We propose AllDayNav, a lifelong self-learning navigation framework that implicitly encodes scene dynamics into the billion-scale parameters of a large model via reinforcement learning, powered by a self-evolving multimodal memory that maintains and updates visual keyframes, semantic descriptions, and temporal context while autonomously generating open-vocabulary instructions, image goals, and structured rewards. Experiments in both synthetic and real-world environments across cross-room, cross-episode, and cross-task scenarios show that AllDayNav achieves success rates approaching $100\%$ and consistently surpasses strong map-based, VLM, and RL baselines in path efficiency and robustness, demonstrating implicit, memory-driven reinforcement learning as a scalable alternative to explicit mapping for reliable lifelong navigation.
Robotics (cs.RO)
arXiv:2606.10927 [cs.RO]
(or arXiv:2606.10927v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.10927 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 232 words
Related coverage
From Seeing to Experiencing: Scaling Navigation Foundation Models with Reinforcement Learning
arXiv · Jun 12, 2026
AgenticRL: Self-Refining Agentic Reinforcement Learning for Vision-Conditioned UAV Navigation
arXiv · Jun 6, 2026
Self-Refining Agentic Reinforcement Learning for Vision-Conditioned UAV Navigation
arXiv · Jun 3, 2026
News
Quantum Computing Report · Jul 25, 2026