Lan-grasp: Using Large Language Models for Semantic Object Grasping and Placement

Understand

In this paper, we propose Lan-grasp, a novel approach towards more appropriate semantic grasping and placing.

• We leverage foundation models to equip the robot with a semantic understanding of object geometry, enabling it to identify the right place to grasp, which parts to avoid, and the natural pose for placement.
• This is an important contribution to grasping and utilizing objects in a more meaningful and safe manner.
• We leverage a combination of a Large Language Model, a Vision-Language Model, and a traditional grasp planner to generate grasps that demonstrate a deeper semantic understanding of the objects.