New Framework Enables Robots to Understand Natural Language Commands for Tabletop Manipulation Tasks
Researchers have developed GRASP, a framework that allows robots to interpret natural-language instructions and perform grasping tasks without task-specific training. The system combines Vision-Language Models with neuro-symbolic planning to translate spoken commands into physical actions grounded in bounding-box detection. This advance could facilitate broader integration of robots into household and industrial environments by reducing the need for extensive pre-training or hard-coded instructions.
GRASP (Grounded Reasoning and Symbolic Planning) is a new robotics framework designed to enable robots to understand and execute natural-language commands for tabletop manipulation. The system leverages pretrained Vision-Language Models to translate abstract spatial concepts—such as "top shelf"—into executable goal states without requiring task-specific fine-tuning or extensive training datasets. Unlike existing approaches that depend on fixed color lists or hard-coded coordinates, GRASP uses a bounding-box detection pipeline to ground language understanding in the physical world. In real-robot trials, the framework achieved a 73.3% success rate across 90 trials at three difficulty levels. The researchers position this work as a step toward open-vocabulary robot manipulation, addressing a key challenge in making robots practical for real-world household and industrial deployment.
What's missing
The study does not discuss failure modes in detail or provide analysis of which types of spatial concepts or task configurations proved most challenging. Additionally, the paper does not compare performance against other state-of-the-art language-conditioned manipulation approaches, limiting assessment of relative performance gains. The computational cost and inference time of the GRASP framework compared to alternatives are not reported.
What different sources said
- arXiv cs.AICenter
Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning
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