Grasp Planning

Grasp planning aims to enable robots to reliably grasp objects, a crucial step for autonomous manipulation. Current research heavily focuses on improving grasp success rates in cluttered, real-world scenarios, employing methods like transformer-based models for scene understanding and vision-language models for instruction-following grasping. These advancements leverage techniques such as differentiable rendering for precise pose estimation and graph neural networks for handling deformable objects, ultimately contributing to more robust and adaptable robotic manipulation in diverse applications.