AERMANI-VLM：基于视觉语言模型的空中 manipulation 的结构提示与推理
AERMANI-VLM: Structured Prompting and Reasoning for Aerial Manipulation with Vision Language Models
AI总结：
本文提出AERMANI-VLM框架，通过分离高层推理与低层控制，利用结构化提示生成自然语言推理轨迹，实现安全可靠的空中 manipulation 任务执行。
摘要：
视觉语言模型（VLMs）的快速发展引发了机器人控制领域的广泛关注，其中自然语言可表达操作目标，而视觉反馈连接感知与行动。然而，直接部署VLM驱动的策略在空中机械臂上仍不安全且不可靠，因为生成的动作往往不一致、易产生幻觉且动态不可行。本文提出AERMANI-VLM，首个将预训练VLM适应于空中 manipulation 的框架，通过分离高层推理与低层控制，无需任务特定微调。该框架将自然语言指令、任务上下文和安全约束编码为结构化提示，引导模型生成自然语言的分步推理轨迹。此推理输出用于从预定义的离散、飞行安全技能库中选择，确保可解释且时间一致的执行。通过将符号推理与物理行动解耦，AERMANI-VLM缓解了幻觉命令并防止了不安全行为，实现了鲁棒的任务完成。我们在仿真和硬件上验证了该框架在多种多步骤拾取和放置任务中的有效性，展示了对之前未见命令、对象和环境的强大泛化能力。
英文摘要：
The rapid progress of vision--language models (VLMs) has sparked growing interest in robotic control, where natural language can express the operation goals while visual feedback links perception to action. However, directly deploying VLM-driven policies on aerial manipulators remains unsafe and unreliable since the generated actions are often inconsistent, hallucination-prone, and dynamically infeasible for flight. In this work, we present AERMANI-VLM, the first framework to adapt pretrained VLMs for aerial manipulation by separating high-level reasoning from low-level control, without any task-specific fine-tuning. Our framework encodes natural language instructions, task context, and safety constraints into a structured prompt that guides the model to generate a step-by-step reasoning trace in natural language. This reasoning output is used to select from a predefined library of discrete, flight-safe skills, ensuring interpretable and temporally consistent execution. By decoupling symbolic reasoning from physical action, AERMANI-VLM mitigates hallucinated commands and prevents unsafe behavior, enabling robust task completion. We validate the framework in both simulation and hardware on diverse multi-step pick-and-place tasks, demonstrating strong generalization to previously unseen commands, objects, and environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
