arXiv:2606.12910v1 Announce Type: cross Abstract: 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 a
The paper addresses the ongoing challenge of integrating advanced AI with robotics, specifically solving for real-time natural-language adaptation in robotic manipulation through efficient neuro-symbolic planning.
This development represents a significant step towards more adaptable and commercially viable autonomous robots, reducing the computational burden and data requirements for complex tasks.
Robots will become more capable of understanding and executing complex tasks from natural language prompts without extensive prior training or massive computational resources.
- · Robotics Companies
- · Logistics & Manufacturing
- · AI/ML Developers
- · Tasks requiring manual manipulation
- · Companies relying on less efficient robotic learning methods
More widespread adoption of AI-driven robotic systems in diverse environments.
Increased demand for skilled AI robotics engineers and a shift in labor markets towards robot interaction management.
Acceleration of autonomous factories and supply chains, potentially leading to new economic models.
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Read at arXiv cs.AI