Grasp Candidate

Grasp candidate generation focuses on algorithmically identifying optimal robot grasp poses for manipulating objects, particularly in cluttered environments or with unseen objects. Current research emphasizes developing robust methods that avoid occlusion, handle diverse object shapes (including thin or flat objects), and improve grasp success rates, employing techniques ranging from model-free learning and geometric analysis to model-based approaches incorporating predictive models of future gripper states. These advancements are crucial for enhancing robotic manipulation capabilities in various applications, from warehouse automation to assistive robotics, by enabling more reliable and efficient object handling.