Embodied exploration is a target-driven process that requires embodied agents to possess fine-grained perception and knowledge-enhanced decision making. While recent attempts leverage MLLMs for exploration due to their strong perceptual and reasoning abilities, we find that MLLM-based embodied agents remain suboptimal in exploring new environments: (i) they rely on profound but stale pre-trained knowledge, (ii) training-based approaches such as imitation learning or reinforcement learning are expensive for long-horizon tasks with sparse outcome rewards, and (iii) frontier-based exploration yields a large, visually nuanced action space that is difficult for MLLMs to make reliable decisions. We address these challenges with ReEXplore, a training-free framework that performs retrospective experience replay to inject distilled, abstract experience at inference time, and hierarchical frontier selection to decompose frontier ranking into coarse-to-fine decisions. Our approach enables robust, traceable, and efficient exploration. Across multiple embodied exploration benchmarks, ReEXplore yields great improvements over strong MLLM baselines, up to 3x higher performance in both success rate and in navigation efficiency under open-source backbones.

翻译：具身探索是一个目标驱动的过程，要求具身智能体具备细粒度感知和知识增强的决策能力。尽管近期研究尝试利用多模态大语言模型（MLLMs）进行探索，因其强大的感知与推理能力，但我们发现基于MLLM的具身智能体在新环境探索中仍存在不足：（1）它们依赖深入但陈旧的预训练知识；（2）基于训练的方法（如模仿学习或强化学习）在长时程任务中因结果奖励稀疏而成本高昂；（3）基于前沿的探索会产生庞大且视觉细节丰富的动作空间，使MLLM难以做出可靠决策。我们通过ReEXplore框架应对这些挑战：该免训练框架在推理时执行回顾性经验回放以注入精炼的抽象经验，并通过分层前沿选择将前沿排序分解为从粗到细的决策。我们的方法实现了鲁棒、可追溯且高效的探索。在多个具身探索基准测试中，ReEXplore相比强大的MLLM基线取得显著提升，在开源骨干网络下，成功率和导航效率最高提升达3倍。