Humanoid robots must adapt their contact behavior to diverse objects and tasks, yet most controllers rely on fixed, hand-tuned impedance gains and gripper settings. This paper introduces HumanoidVLM, a vision-language driven retrieval framework that enables the Unitree G1 humanoid to select task-appropriate Cartesian impedance parameters and gripper configurations directly from an egocentric RGB image. The system couples a vision-language model for semantic task inference with a FAISS-based Retrieval-Augmented Generation (RAG) module that retrieves experimentally validated stiffness-damping pairs and object-specific grasp angles from two custom databases, and executes them through a task-space impedance controller for compliant manipulation. We evaluate HumanoidVLM on 14 visual scenarios and achieve a retrieval accuracy of 93%. Real-world experiments show stable interaction dynamics, with z-axis tracking errors typically within 1-3.5 cm and virtual forces consistent with task-dependent impedance settings. These results demonstrate the feasibility of linking semantic perception with retrieval-based control as an interpretable path toward adaptive humanoid manipulation.

翻译：人形机器人必须使其接触行为适应不同的物体和任务，然而大多数控制器依赖于固定、手动调整的阻抗增益和夹持器设置。本文介绍了HumanoidVLM，一个视觉-语言驱动的检索框架，它使Unitree G1人形机器人能够直接从以自我为中心的RGB图像中选择适合任务的笛卡尔阻抗参数和夹持器配置。该系统将一个用于语义任务推理的视觉-语言模型与一个基于FAISS的检索增强生成（RAG）模块相结合，该模块从两个定制数据库中检索经过实验验证的刚度-阻尼对和针对特定物体的抓取角度，并通过一个任务空间阻抗控制器执行它们，以实现顺应性操作。我们在14个视觉场景上评估了HumanoidVLM，检索准确率达到93%。真实世界实验显示出稳定的交互动力学，z轴跟踪误差通常在1-3.5厘米以内，虚拟力与任务相关的阻抗设置一致。这些结果证明了将语义感知与基于检索的控制相连接作为实现自适应人形机器人操作的可解释路径的可行性。