HiRO-Nav: Hybrid ReasOning Enables Efficient Embodied Navigation

📄 arXiv: 2604.08232v1 📥 PDF

作者: He Zhao, Yijun Yang, Zichuan Lin, Deheng Ye, Chunyan Miao

分类: cs.AI

发布日期: 2026-04-09

💡 一句话要点

提出HiRO-Nav以解决长时间导航任务中的推理效率问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 具身导航 推理模型 行动熵 强化学习 智能决策 机器人技术 多模态输入

📋 核心要点

  1. 现有的具身导航方法在处理复杂环境时，推理效率低下，难以平衡反应与深度思考。
  2. HiRO-Nav通过自适应判断行动熵，决定何时进行推理，从而优化决策过程。
  3. 在CHORES-$ ext{S}$ ObjectNav基准测试中，HiRO-Nav在成功率和计算效率上优于现有基线方法。

📝 摘要（中文）

基于大型推理模型（LRMs）的具身导航代理能够处理复杂的多模态环境输入，并在每一步进行扎根推理，以改善长时间任务的顺序决策。然而，如何智能高效地利用LRMs的推理能力仍然是一个关键问题。为此，本文提出了HiRO-Nav代理，能够根据自身的行动熵自适应地决定每一步是否进行推理。通过分析行动熵的演变，发现只有少部分高熵动作能引导代理进入新场景或关键物体。我们设计了一种混合监督微调和在线强化学习的训练流程，显著降低了计算开销，同时提升了决策质量。实验结果表明，HiRO-Nav在成功率和令牌效率之间取得了更好的平衡。

🔬 方法详解

问题定义：本文旨在解决具身导航任务中推理效率低下的问题，现有方法在复杂场景中难以有效平衡反应与深度思考，导致决策质量下降。

核心思路：HiRO-Nav通过分析代理的行动熵变化，自适应地决定在每一步是否进行推理。高熵动作通常与任务成功相关，因此优先激活推理以提升决策质量。

技术框架：HiRO-Nav的整体架构包括混合监督微调作为冷启动，随后进行在线强化学习。训练过程中，代理根据行动熵动态调整推理策略。

关键创新：HiRO-Nav的核心创新在于其自适应推理机制，能够根据行动熵的变化智能选择推理时机，与传统方法相比显著降低了计算开销。

关键设计：在训练过程中，采用混合监督学习和强化学习相结合的策略，设计了特定的损失函数以优化高熵动作的决策质量。

🖼️ 关键图片

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📊 实验亮点

在CHORES-$ ext{S}$ ObjectNav基准测试中，HiRO-Nav的成功率和令牌效率均优于密集思考和无思考基线，展示了其在长时间导航任务中的显著优势。具体而言，HiRO-Nav在成功率上提升了X%，而计算效率提高了Y%。

🎯 应用场景

HiRO-Nav的研究成果在智能机器人、自动驾驶和虚拟助手等领域具有广泛的应用潜力。通过提高具身导航的推理效率，该技术能够使机器人在复杂环境中更有效地进行任务执行，提升用户体验和系统可靠性。

📄 摘要（原文）

Embodied navigation agents built upon large reasoning models (LRMs) can handle complex, multimodal environmental input and perform grounded reasoning per step to improve sequential decision-making for long-horizon tasks. However, a critical question remains: \textit{how can the reasoning capabilities of LRMs be harnessed intelligently and efficiently for long-horizon navigation tasks?} In simple scenes, agents are expected to act reflexively, while in complex ones they should engage in deliberate reasoning before acting.To achieve this, we introduce \textbf{H}ybr\textbf{i}d \textbf{R}eas\textbf{O}ning \textbf{Nav}igation (\textbf{HiRO-Nav}) agent, the first kind of agent capable of adaptively determining whether to perform thinking at every step based on its own action entropy. Specifically, by examining how the agent's action entropy evolves over the navigation trajectories, we observed that only a small fraction of actions exhibit high entropy, and these actions often steer the agent toward novel scenes or critical objects. Furthermore, studying the relationship between action entropy and task completion (i.e., Q-value) reveals that improving high-entropy actions contributes more positively to task success.Hence, we propose a tailored training pipeline comprising hybrid supervised fine-tuning as a cold start, followed by online reinforcement learning with the proposed hybrid reasoning strategy to explicitly activate reasoning only for high-entropy actions, significantly reducing computational overhead while improving decision quality. Extensive experiments on the \textsc{CHORES}-$\mathbb{S}$ ObjectNav benchmark showcases that HiRO-Nav achieves a better trade-off between success rates and token efficiency than both dense-thinking and no-thinking baselines.