New Framework Enables Robots to Understand Natural Language Commands for Tabletop Manipulation Tasks
Researchers have introduced GRASP (Grounded Reasoning and Symbolic Planning), a robotics framework that translates natural-language commands into physical grasping actions using a pretrained Vision-Language Model and bounding-box detection. The system is designed to address limitations of current approaches that are computationally expensive or require thousands of training demonstrations. It achieved a 73.3% overall success rate across 90 real-robot trials at three difficulty levels, requiring no task-specific fine-tuning.
A team of researchers has presented GRASP, a neuro-symbolic planning framework aimed at enabling robots to respond to open-vocabulary natural-language instructions for tabletop manipulation tasks. The system uses a pretrained Vision-Language Model (VLM) to convert language queries into symbolic goal states, which are then grounded in the physical environment through a bounding-box detection pipeline. Unlike prior approaches that depend on fixed color lists or hard-coded spatial coordinates, GRASP can interpret abstract spatial concepts such as 'top shelf' without additional fine-tuning on task-specific data. The framework was evaluated across 90 real-robot trials spanning three difficulty levels, achieving a 73.3% overall success rate. The authors position GRASP as a step toward practical robot integration in household and industrial settings, where adaptability to natural human instructions in real time is essential. The work is submitted under robotics, AI, computer vision, and systems and control subject areas on arXiv.
What's missing
It is unclear how the system performs with ambiguous or highly abstract language instructions, what the failure modes are, or how GRASP compares quantitatively to specific competing baselines on the same tasks.
What different sources said
- arXiv cs.AICenter
Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning
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