Hierarchical vision-language-action (Hi-VLA) systems have emerged as a promising paradigm for complex robot manipulation, by using high-level VLM planners to decompose tasks into language subgoals executed by low-level VLA controllers. Despite recent empirical progress, there is a lack of unified design principles for these systems: existing Hi-VLA systems differ in how they choose and connect planners, controllers, mechanisms to switch between the two, and how observations and memory are represented in the planner. In this paper, we present a systematic study of Hi-VLA design for robot manipulation. We unify representative Hi-VLA agents under an options-style control framework and benchmark core design choices across short-horizon, long-horizon, and reasoning-intensive tasks. Our analysis distills practical principles for building Hi-VLA systems, showing how model choices and interface mechanisms jointly shape performance. Applying these principles yields a substantially stronger system than either flat VLA control or a naively designed hierarchy, across experiments both in simulation and on a real ALOHA robot. Overall, our results provide a foundation for building more capable, robust, and principled hierarchical VLA agents. More information and video at jiahenghu.github.io/hi-vla.

翻译：分层视觉-语言-动作（Hi-VLA）系统已成为复杂机器人操作领域的一种重要范式，其通过高层VLM规划器将任务分解为语言子目标，并由低层VLA控制器执行。尽管近期实证研究取得了进展，但此类系统尚缺乏统一的设计原则：现有Hi-VLA系统在规划器与控制器选择、两者切换机制设计、以及规划器中观测与记忆表征方式等方面存在显著差异。本文针对机器人操作中的Hi-VLA设计开展系统性研究。我们将代表性Hi-VLA智能体统一整合至基于选项的控制框架下，并在短时域、长时域及推理密集型任务中对其核心设计选择进行基准测试。通过分析，我们提炼出构建Hi-VLA系统的实用原则，揭示了模型选择与接口机制如何共同决定系统性能。应用这些原则所构建的系统，在仿真环境与真实ALOHA机器人实验中，均显著优于平面VLA控制或朴素分层设计。总体而言，本研究为构建更强大、更鲁棒且更具原则性的分层VLA智能体奠定了坚实基础。更多信息与视频请见 jiahenghu.github.io/hi-vla。