  Despite significant progress in robotic systems for operation within
human-centric environments, existing models still heavily rely on explicit
human commands to identify and manipulate specific objects. This limits their
effectiveness in environments where understanding and acting on implicit human
intentions are crucial. In this study, we introduce a novel task: reasoning
grasping, where robots need to generate grasp poses based on indirect verbal
instructions or intentions. To accomplish this, we propose an end-to-end
reasoning grasping model that integrates a multi-modal Large Language Model
(LLM) with a vision-based robotic grasping framework. In addition, we present
the first reasoning grasping benchmark dataset generated from the GraspNet-1
billion, incorporating implicit instructions for object-level and part-level
grasping, and this dataset will soon be available for public access. Our
results show that directly integrating CLIP or LLaVA with the grasp detection
model performs poorly on the challenging reasoning grasping tasks, while our
proposed model demonstrates significantly enhanced performance both in the
reasoning grasping benchmark and real-world experiments.
