MoMaStage：基于技能-状态图的规划与闭环执行的长时域室内移动操作
MoMaStage: Skill-State Graph Guided Planning and Closed-Loop Execution for Long-Horizon Indoor Mobile Manipulation
AI总结：
MoMaStage通过结构化视觉-语言框架和闭环执行机制，实现长时域室内移动操作的高效规划与执行。
摘要：
室内移动操作（MoMA）使机器人能够将自然语言指令转化为物理动作，但长时域执行仍然具有挑战性，因为存在级联误差和在不同环境中有限的泛化能力。基于学习的方法往往难以在长时域内保持逻辑一致性，而依赖显式场景表示的方法则会引入刚性的结构假设，从而在动态环境中降低适应性。为了解决这些限制，我们提出了MoMaStage，这是一个结构化的视觉-语言框架，用于长时域MoMA，消除了对显式场景映射的需求。MoMaStage将视觉-语言模型（VLM）嵌入到分层技能库和拓扑感知的技能-状态图中，在可行的转换空间内约束任务分解和技能组合。这种结构化定位确保生成的计划在逻辑上保持一致，并在代理的演变物理状态中拓扑有效。为了增强鲁棒性，MoMaStage集成了闭环执行机制，该机制监控本体反馈，并在检测到偏差时触发图约束的语义重计划，保持计划的技能与物理结果的一致性。在物理丰富的模拟和真实世界环境中的大量实验表明，MoMaStage优于最先进的基线，实现了显著更高的规划成功率，减少了令牌开销，并在长时域移动操作中显著提高了整体任务成功率。视频演示可在项目网站上查看：https://chenxuli-cxli.github.io/MoMaStage/。
英文摘要：
Long-horizon indoor mobile manipulation (MoMa) requires robots to execute extended navigation-manipulation sequences whose feasibility depends on state changes induced by preceding skills. Vision-language models (VLMs) can decompose instructions into plausible skill sequences, but they do not reliably track such cumulative embodiment constraints or revise a plan when execution deviates from expectation. We present MoMaStage, a map-light framework for state-consistent planning and closed-loop execution in long-horizon indoor MoMa. MoMaStage couples a frozen VLM with robot execution through three mechanisms: (i) a hierarchical library of grounded, executable skills and a topology-only projection of a Skill-State Graph (SSG) that constrains the VLM's planning space; (ii) an SSG verifier that propagates scene-region and gripper-occupancy state to reject infeasible plans before execution; and (iii) an event-driven monitor that triggers graph-grounded repair only when an observed outcome invalidates the remaining plan. The SSG captures the compact embodiment state needed for skill sequencing without requiring a dense scene map, while geometric and contact-level conditions remain within the underlying controllers. Experiments in physics-rich simulation and on a real mobile manipulator show that MoMaStage improves planning validity and long-horizon execution survival over the evaluated baselines, while reducing latency and model token consumption.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
