无需训练的多智能体导航系统，靠视觉语言模型实现长期探索与目标感知。
AnyGoal: Vision-Language Guided Multi-Agent Exploration for Training-Free Lifelong Navigation
- 用视觉语言模型和2D高斯贝叶斯价值图实现无监督持续学习导航
- 双智能体在未见场景下达成52.4%子任务成功率，较基线提升27.5个百分点
- 适合关注开放词汇、长期自主导航的科研与工程人员
端到端导航策略在分布外场景、类别或目标模态转移时性能显著下降。模块化系统如Modular GOAT受限于封闭集物体检测召回率，而3D快照记忆系统（如3D-Mem）积累密集且视角依赖的表征，维护成本高。本文提出AnyGoal，一种无需训练的多机器人架构，以视觉语言模型（VLM）为核心驱动基于前沿的探索，并通过共享的2D高斯贝叶斯价值图（BVM）协调智能体。BVM对每个像素维护目标相关性的后验分布（mu, sigma²），通过深度锥掩码加权融合VLM得分，且在子任务间不重置，实现终身证据累积。前沿按VLM评分软投票与BVM上的贝叶斯UCB凸组合排序。采用带空间分离惩罚和承诺滞后的贪婪分配器，在无中心控制器下分发前沿。在GOAT-Bench验证集未见划分（360个实验，2,669个子任务）上，双智能体系统达到52.4%子任务成功率（Subtask SR）与12.7%成功路径长（SPL），为严格物理环境下（离散0.25米步长、无传送、42度水平视场）最先进结果，较Modular GOAT提升27.5个百分点（24.9%）。单智能体达41.9%子任务成功率，表明增益源于决策架构。四路感知消融显示，开放词汇检测将主要失败模式从探索转向目标验证。
原文摘要 · Abstract (English)
End-to-end navigation policies trained on large simulation corpora degrade sharply when transferred to out-of-distribution scenes, categories, or goal modalities. Modular pipelines such as Modular GOAT are bottlenecked by closed-set object detection recall, while 3D snapshot-memory systems (e.g. 3D-Mem) accumulate dense, view-dependent representations that are heavy to maintain. We present AnyGoal, a training-free multi-robot architecture that places a Vision-Language Model (VLM) at the core of frontier-based exploration and coordinates agents through a shared 2D Gaussian Bayesian Value Map (BVM). The BVM maintains a per-pixel (mu, sigma^2) posterior over goal relevance, updated via precision-weighted fusion of VLM scores through a depth-cone mask, and is never reset between subtasks, yielding lifelong evidence accumulation. Frontiers are ranked by a convex blend of a VLM-as-judge softmax and a Bayesian UCB term on the BVM. A greedy allocator with spatial-separation penalty and commitment hysteresis distributes frontiers across agents without a centralized controller. On the full GOAT-Bench val unseen split (360 episodes, 2,669 subtasks), our dual-agent system achieves 52.4% Subtask SR at 12.7% SPL--state of the art under the strict physical regime (discrete 0.25 m steps, no teleportation, 42 deg HFOV) and a +27.5 pp improvement over Modular GOAT (24.9%). Single-agent AnyGoal achieves 41.9% Subtask SR, showing gains arise from the decision architecture. A four-way perception ablation shows that open-vocabulary detectors shift the dominant failure mode from exploration to goal verification.
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