这篇论文搞了个EvolveNav，让机器人自己从失败中学习，零样本导航成功率直接涨了10%，而且没走那么多冤枉路。
EvolveNav 提出了一种自演化框架，用于零样本物体目标导航，无需预先训练。该方法通过构建代理规则记忆，从过往轨迹中提取可操作知识，并采用基于上置信界（UCB）的检索策略平衡语义相关性和历史成功率。还引入了记忆引导的前置反思模块，在行动前预测潜在结果，减少低效探索。实验表明，EvolveNav 在多个基准上超越现有零样本基线，成功率提升 10.1%，同时减少了不必要的步骤。
EvolveNav: Proactive Preflection and Self-Evolving Memory for Zero-Shot Object Goal Navigation
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 strategy based on upper confidence bound, selecting effective rules by balancing semantic relevance and historical success. In addition, we introduce a memory-guided preflection module that forecasts potential outcomes before action, reducing inefficient exploration. Extensive experiments show that our method outperforms existing zero-shot baselines, achieving a 10.1\% improvement in success rate with fewer unnecessary steps.