Multi-GraspLLM introduces a breakthrough approach that enables different robotic hands to generate semantically appropriate grasps based on natural language instructions.

• First large-scale multi-hand grasp dataset with automated contact annotations
• Multimodal large language model that interprets verbal commands for robotic grasping
• Supports multiple hand types and diverse object manipulation scenarios
• Demonstrates superior performance in generating contextually appropriate grasps

This innovation addresses a critical challenge in industrial automation by enabling more intuitive human-robot interaction. Factory robots equipped with this technology could understand and execute complex grasping tasks through simple verbal instructions, dramatically improving manufacturing flexibility and efficiency.

Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation