RoboQuest: Generalist Physical Agents that Search, Inspect and Test
作者: Liu Renhang, Navonil Majumder, Tej Deep Pala, Soujanya Poria
分类: cs.RO, cs.AI, cs.CV
发布日期: 2026-10-07
💡 一句话要点
提出RoboQuest以解决物理智能体在不确定环境中的探索问题
🎯 匹配领域: 支柱一：机器人控制 (Robot Control) 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 物理智能体 主动探索 多模态模型 任务导向 不确定性处理 移动操作 机器人技术
📋 核心要点
- 现有方法在不熟悉环境中执行任务时，智能体常常无法主动获取必要的信息，导致任务失败。
- 本文提出RoboQuest基准，要求智能体通过物理交互主动获取任务相关信息，并根据证据调整行动。
- 实验结果显示，最佳智能体在23%的实验中成功，且智能体在探索过程中常常未能有效应对干扰。
📝 摘要（中文）
近年来，多模态基础模型的进展使得物理智能体能够执行多种操作任务。然而，在不熟悉的环境中，智能体可能需要通过交互来获取任务相关信息。为此，本文提出了RoboQuest，一个面向目标导向的实体探索基准，要求智能体通过物理交互主动获取信息，并根据获得的证据调整后续行动。RoboQuest包含十个移动操作任务，围绕搜索、基于操作的检查和交互测试三种不确定性展开。我们评估了五种前沿多模态智能体，结果显示最佳智能体仅在23%的实验中成功，而微调后的策略几乎从未成功。我们的失败分析表明，智能体常常过早停止探索，未能观察到完成任务所需的证据。
🔬 方法详解
问题定义：本文旨在解决智能体在不确定环境中执行任务时的信息获取不足问题。现有方法往往无法有效处理未知信息，导致任务失败率高。
核心思路：RoboQuest基准要求智能体通过物理交互主动探索环境，获取任务相关信息，并根据这些信息调整后续行动，从而提高任务成功率。
技术框架：RoboQuest的整体架构包括三个主要模块：搜索模块、检查模块和测试模块。智能体通过这些模块进行信息获取、证据分析和决策制定。
关键创新：RoboQuest的创新在于引入了主动探索的概念，智能体不仅执行任务，还需在执行过程中主动获取信息，这与传统的被动执行方法有本质区别。
关键设计：在设计中，智能体的决策过程依赖于观察到的证据，采用了特定的损失函数来优化探索策略，并通过微调策略来提升智能体的执行能力。实验中还考虑了探索过程中的干扰管理。
🖼️ 关键图片
📊 实验亮点
实验结果表明，最佳智能体在23%的实验中成功，而微调后的策略几乎未能成功。失败分析显示，智能体在探索过程中常常过早停止，未能充分利用可用信息，导致任务完成率低。
🎯 应用场景
RoboQuest的研究成果可广泛应用于机器人领域，特别是在需要自主探索和决策的场景，如搜索与救援、工业自动化和智能家居等。通过提高智能体在不确定环境中的适应能力，未来可能推动更智能的机器人系统的发展。
📄 摘要（原文）
Recent advances in multimodal foundation models have made them capable generalist physical agents for a range of manipulation tasks. However, successful operation in an unfamiliar environment may require an agent to seek task-relevant information through interaction when it is absent from the observations: it may need to determine where a relevant object is, inspect an unobserved property, or discover the effect of an unfamiliar tool. We thus introduce RoboQuest, a benchmark for goal-directed embodied exploration, where agents must actively acquire task-relevant information through physical interaction, use the resulting evidence to adapt subsequent actions, and autonomously decide when to commit to task completion. RoboQuest comprises ten mobile manipulation tasks centered on three forms of uncertainty: search, manipulation-based inspection, and interactive testing. We evaluate five frontier multimodal agents through a common visuomotor interface, as well as a $π_{0.5}$ policy fine-tuned on the full-episode demonstrations we release. The best agent succeeds in only 23\% of the episodes, and the fine-tuned policy almost never succeeds. Isolated tests of the execution skills the tasks are built from, with the hidden information supplied, show that the agents can carry out most of the required actions, and our failure analysis attributes only a minority of the failures to execution. Our failure analysis further finds that the agents often stop exploring too early as they make decisions before observing the required evidence for task completion. We also find that agents rarely prevent or repair the disturbances caused by their exploration. Moreover, learning by trial and error remains difficult for most models.