评估VLMs在机器人运动中的空间推理能力：迈向具有运动偏好的机器人规划的一步
Evaluating VLMs' Spatial Reasoning Over Robot Motion: A Step Towards Robot Planning with Motion Preferences
AI总结：
本文评估了四种先进VLMs在机器人运动中的空间推理能力，探讨了运动偏好（如物体接近性和路径风格）的处理，并分析了准确率与计算成本的权衡。
摘要：
理解用户指令和周围环境中的物体空间关系对于智能机器人系统在各种任务中协助人类至关重要。Vision-Language Models (VLMs) 的自然语言和空间推理能力有潜力增强机器人规划器在新任务、物体和运动规范上的泛化能力。尽管基础模型已应用于任务规划，但尚不清楚它们在执行用户偏好或约束（如与物体的距离、拓扑属性或运动风格偏好）所需的空间推理能力方面有多强。本文通过四种不同的查询方法评估了四种最先进的VLMs在机器人运动上的空间推理能力。我们的结果表明，使用最有效的查询方法，Qwen2.5-VL在零样本情况下达到71.4%的准确率，在微调后的小模型上达到75%的准确率，而GPT-4o的性能较低。我们评估了两种类型的运动偏好（物体接近性和路径风格），并分析了在token数量上的准确率与计算成本的权衡。这项工作展示了VLMs与机器人运动规划管道整合的潜力。
英文摘要：
Understanding user instructions and object spatial relations in surrounding environments is crucial for intelligent robot systems to assist humans in various tasks. The natural language and spatial reasoning capabilities of Vision-Language Models (VLMs) have the potential to enhance the generalization of robot planners on new tasks, objects, and motion specifications. While foundation models have been applied to task planning, it is still unclear the degree to which they have the capability of spatial reasoning required to enforce user preferences or constraints on motion, such as desired distances from objects, topological properties, or motion style preferences. In this paper, we evaluate the capability of four state-of-the-art VLMs at spatial reasoning over robot motion, using four different querying methods. Our results show that, with the highest-performing querying method, Qwen2.5-VL achieves 71.4% accuracy zero-shot and 75% on a smaller model after fine-tuning, and GPT-4o leads to lower performance. We evaluate two types of motion preferences (object-proximity and path-style), and we also analyze the trade-off between accuracy and computation cost in number of tokens. This work shows some promise in the potential of VLM integration with robot motion planning pipelines.
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。
