For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural-language prompts in real time. Although Vision-Language Models (VLMs) have enabled zero-shot generalization in robot task and motion planning (TAMP), current state-of-the-art approaches often remain computationally "heavyweight" or require extensive training on thousands of demonstrations. We present GRASP (Grounded Reasoning and Symbolic Planning), a framework designed as a step toward open-vocabulary tabletop manipulation. Our approach leverages a pretrained VLM to translate natural-language queries into neuro-symbolic goal states, grounded in the physical world via a bounding-box detection pipeline. Unlike methods that rely on fixed color lists or hard-coded coordinates, GRASP enables robots to interpret abstract spatial concepts such as "top shelf" and execute tasks without additional fine-tuning. We achieve 73.3% overall success across 90 real-robot trials at three difficulty levels, requiring no task-specific training.

翻译：要使机器人有效融入家庭或工业环境，机器必须能够实时适应自然语言指令。尽管视觉语言模型（VLM）已实现机器人任务与运动规划（TAMP）中的零样本泛化，当前最先进的方法通常仍计算开销庞大，或需要在数千个演示样本上进行大量训练。我们提出GRASP（接地推理与符号规划）框架，旨在向开放词汇桌面操作迈进一步。该方法利用预训练VLM将自然语言查询转化为神经符号目标状态，并通过边界框检测流水线将其锚定至物理世界。与依赖固定颜色列表或硬编码坐标的方法不同，GRASP使机器人能够解读“顶层货架”等抽象空间概念，并在无需额外微调的情况下执行任务。在90次真实机器人试验中，我们在三个难度级别上实现了73.3%的总体成功率，且无需任务特定训练。