Maestro: Orchestrating Robotics Modules with Vision-Language Models for Zero-Shot Generalist Robots
This work addresses the problem of building adaptable and extensible generalist robots for manipulation tasks, offering a novel alternative to large end-to-end training approaches.
The paper tackles the challenge of creating generalist robots by using vision-language models to dynamically compose specialized robotics modules into programmatic policies, achieving superior zero-shot performance on manipulation tasks compared to existing VLA models.
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.