EvolveNav: Proactive Preflection and Self-Evolving Memory for Zero-Shot Object Goal Navigation
arXiv:2606.18235v1 Announce Type: new Abstract: Zero-Shot Object-Goal Navigation (ZS-OGN) requires embodied agents to explore and locate target objects without any prior training. To this end, recent methods leverage foundation models. But they typically rely on static priors and lack adaptation, which leads to repeated errors and costly trial and error. In this paper, we propose a self-evolving ZS-OGN framework that enables continuous test-time improvement. Specifically, we build an agentic rule memory by extracting actionable knowledge from past trajectories. Then, we propose a retrieval str
The rapid advancement of foundation models and the increasing demand for autonomous agents highlight the limitations of static AI in dynamic environments, pushing for continuous improvement mechanisms.
This development in self-evolving memory for zero-shot object navigation could significantly enhance the robustness and adaptability of AI agents, reducing error rates and operational costs in complex real-world scenarios.
AI agents will be able to learn and adapt at test-time without explicit retraining, directly addressing the challenge of costly trial-and-error in novel environments, leading to faster deployment and reduced reliance on pre-trained static knowledge.
- · AI/robotics companies
- · Logistics and supply chain
- · Service robotics
- · Embodied AI researchers
- · Developers relying solely on static, pre-trained models
- · Manual navigation systems
Embodied AI agents become more efficient and reliable in unknown environments.
Reduced need for extensive re-training or human intervention in dynamically changing operational contexts.
Accelerated deployment of autonomous systems into diverse and less structured real-world applications, expanding market reach.
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Read at arXiv cs.AI