用户用自然语言就能定制机器人导航行为。
Hey Robot! Personalizing Robot Navigation through Model Predictive Control with a Large Language Model
- 用视觉语言模型理解用户指令或环境图，动态调整控制器参数。
- 零样本适配，无需训练即可在仿真和真实场景中生效。
- 适合希望个性化控制机器人的非专业用户使用。
机器人导航技术广泛应用于仓库、医院等场景。现有方法通常无法让用户自定义机器人行为优先级，可能导致不恰当行为（如医院内高速行驶）。本文提出一种新方法，通过用户提供的自然语言指令实时调整机器人运动行为。该零样本方法利用现有视觉语言模型解析用户文本或环境图像，据此生成成本函数并重配置模型预测控制器参数，将自然语言指令转化为具体运动策略。实验在仿真与真实地面机器人上验证了该方法的有效性，可在多种复杂动态环境中实现安全高效的导航。结果表明，该方法能灵活响应不同用户需求，提升人机交互的可操作性。
原文摘要 · Abstract (English)
Robot navigation methods allow mobile robots to operate in applications such as warehouses or hospitals. While the environment in which the robot operates imposes requirements on its navigation behavior, most existing methods do not allow the end-user to configure the robot's behavior and priorities, possibly leading to undesirable behavior (e.g., fast driving in a hospital). We propose a novel approach to adapt robot motion behavior based on natural language instructions provided by the end-user. Our zero-shot method uses an existing Visual Language Model to interpret a user text query or an image of the environment. This information is used to generate the cost function and reconfigure the parameters of a Model Predictive Controller, translating the user's instruction to the robot's motion behavior. This allows our method to safely and effectively navigate in dynamic and challenging environments. We extensively evaluate our method's individual components and demonstrate the effectiveness of our method on a ground robot in simulation and real-world experiments, and across a variety of environments and user specifications.
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