ForagerSim: Evaluating Embodied Map-Based Reasoning in Large-Scale Dynamic Worlds

📄 arXiv: 2610.26395v1 📥 PDF

作者: Tomáš Musil, Martin Kotík, Michal Vavrečka, Karla Štěpánová, Sebastian Scherer, Martin Saska

分类: cs.RO, cs.LG

发布日期: 2026-10-19

备注: Code and data: https://github.com/MrTomzor/foragersim

💡 一句话要点

提出ForagerSim以解决动态环境中地图推理问题

🎯 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AI 地图推理 动态环境 仿真平台 强化学习 模块化设计 机器人导航

📋 核心要点

  1. 现有的具身AI系统依赖手工设计的映射管道，无法应对动态环境，且学习策略通常不形成地图式表示。
  2. 本文提出ForagerSim，一个模块化的仿真环境，支持多种具身任务并要求地图推理，旨在提高AI系统的灵活性。
  3. 实验比较了强化学习基线、简单工程机器人堆栈和人类玩家在不同环境中的表现，结果显示地图推理是性能瓶颈。

📝 摘要（中文）

动物能够构建认知地图并基于此进行导航，而现有的具身AI系统在动态环境中表现不佳。为此，本文提出ForagerSim，一个专注于具身任务的跨学科研究平台和仿真环境，支持从移动机器人搜索救援到动物觅食的多种场景。ForagerSim具有高度模块化的特点，允许调整感知、规模、拓扑、动态和任务等多个难度轴。实验结果表明，在简化的2D感知下，学习策略在简单拓扑中表现良好，但在复杂环境中则不及人类表现，显示出地图推理是瓶颈。

🔬 方法详解

问题定义：本文旨在解决现有具身AI系统在动态环境中地图推理能力不足的问题。现有方法往往依赖手工设计的映射管道，无法适应未建模的动态情况，且学习策略缺乏地图式表示。

核心思路：ForagerSim通过提供一个模块化的仿真环境，使得AI系统能够在多样化的任务中学习和使用地图式表示，模拟动物的认知能力。

技术框架：ForagerSim的整体架构包括多个模块，涵盖感知（从抽象的2D感知到完整的RGB或LiDAR）、规模（从房间级到多公里级）、拓扑（开放场地到复杂迷宫）、动态（烟雾、碎片、传感器运动失败等）和任务（从简单的2D覆盖到受启发的觅食任务）。

关键创新：ForagerSim的创新在于其高度模块化和灵活性，允许研究者在多个维度上调整难度，这与现有方法的固定性形成鲜明对比。

关键设计：在实验中，使用了不同的感知简化策略，设置了多种任务和环境条件，以评估学习策略和人类表现的差异。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，在简化的2D感知条件下，学习策略在简单拓扑中能够有效执行多目标搜索和水井交替任务，但在更复杂的环境中表现不及人类，表明地图推理能力是当前方法的主要瓶颈。

🎯 应用场景

ForagerSim的潜在应用领域包括机器人导航、自动化搜索与救援、以及生物启发的算法研究。通过模拟复杂的动态环境，研究者可以更好地理解和开发具身AI系统，提高其在现实世界中的适应能力和智能水平。

📄 摘要（原文）

Animals build cognitive maps of vast environments, use them to perform map-based navigation, and might even be reusing the same circuitry used in spatial reasoning for creating and using maps of arbitrary task-spaces. In contrast, current embodied AI systems either rely on hand-engineered mapping pipelines, which break in the presence of unmodeled dynamics and are restricted to metric space, or on learned policies that rarely form map-like representations at all. Developing architectures that could learn to build and use map-like representations as flexibly as animals is, however, difficult because few embodied AI benchmarks actually require map-based reasoning. To address this, we present ForagerSim --- an interdisciplinary research platform and simulation environment focused on embodied tasks that require map-based reasoning, with scenarios ranging from mobile-robot search and rescue to animal foraging in both complex mazes and vast, potentially infinite open areas. ForagerSim is highly modular and allows modifying independent axes of difficulty related to perception (abstract 2D sensing to full RGB or LiDAR), scale (room-scale to multi-km scale), topology (open-fields to complex mazes), dynamics (smoke, debris, sensorimotor failures and others) and tasks (from simple 2D coverage to foraging-inspired well-alternation tasks). We compare reinforcement learning baselines with a simple engineered robotics stack and a human player on 8 example worlds which show how different difficulty axes affect existing methods. Our results show that, when sensing is simplified to 2D, learned policies can perform multi-object search and well-alternation tasks surprisingly well in simple topologies, but they fall short of human performance in larger or more complex-topology environments where map-based baselines perform well, suggesting that map-based reasoning is the bottleneck in these scenarios.