Maestro：利用视觉-语言模型编排机器人模块以实现零样本通用机器人
Maestro: Orchestrating Robotics Modules with Vision-Language Models for Zero-Shot Generalist Robots
AI总结：
本研究提出Maestro系统，以VLM编码智能体动态组合感知、规划、控制模块生成可编程策略，在零样本操作任务上超越现有VLA模型，且具备高扩展性与适配性。
摘要：
当前通用机器人领域探索最深入的路径，核心是收集规模越来越大的“观测输入-动作输出”机器人数据集来训练大型端到端模型，复刻了已在视觉-语言模型（VLM）中验证有效的方案。我们选择了一条少有人走的路线：直接围绕VLM构建通用策略，通过一组精心筛选的感知、规划与控制模块所封装的特定机器人能力，来增强VLM的通用能力。在Maestro系统中，一个VLM编码智能体会根据当前任务与场景，动态地将这些模块组合成可编程策略。Maestro的架构得益于精简的闭环接口（无过多人工施加的结构约束），以及全面多样的工具库。因此，在具有挑战性的操作技能零样本性能上，它大幅超越了当前的视觉-语言-动作（VLA）模型。此外，Maestro易于扩展以集成新模块，易于编辑以适配新的机器人载体（如搭载机械臂的四足机器人），甚至能通过少量本地代码编辑，从极少的真实世界经验中快速适配。
英文摘要：
Today's best-explored routes towards generalist robots center on collecting ever larger "observations-in actions-out" robotics datasets to train large end-to-end models, copying a recipe that has worked for vision-language models (VLMs). We pursue a road less traveled: building generalist policies directly around VLMs by augmenting their general capabilities with specific robot capabilities encapsulated in a carefully curated set of perception, planning, and control modules. In Maestro, a VLM coding agent dynamically composes these modules into a programmatic policy for the current task and scenario. Maestro's architecture benefits from a streamlined closed-loop interface without many manually imposed structural constraints, and a comprehensive and diverse tool repertoire. As a result, it largely surpasses today's VLA models for zero-shot performance on challenging manipulation skills. Further, Maestro is easily extensible to incorporate new modules, easily editable to suit new embodiments such as a quadruped-mounted arm, and even easily adapts from minimal real-world experiences through local code edits.
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。
