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arXiv · 2511.00917

Maestro: Orchestrating Robotics Modules with Vision-Language Models for Zero-Shot Generalist Robots

Junyao Shi·Rujia Yang·Kaitian Chao·Selina Bingqing Wan·Yifei Shao·Jiahui Lei·Jianing Qian·Long Le·Pratik Chaudhari·Kostas Daniilidis·Chuan Wen·Dinesh Jayaraman
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Abstract

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.

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Junyao Shi, Rujia Yang, Kaitian Chao, Selina Bingqing Wan, Yifei Shao, Jiahui Lei, Jianing Qian, Long Le, Pratik Chaudhari, Kostas Daniilidis, Chuan Wen, Dinesh Jayaraman. 2025-11-02. Maestro: Orchestrating Robotics Modules with Vision-Language Models for Zero-Shot Generalist Robots. https://arxiv.org/abs/2511.00917

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