Peg in Hole

Robotic peg-in-hole insertion, a fundamental challenge in robotic manipulation, focuses on reliably inserting a peg into a hole despite uncertainties in alignment and pose. Current research emphasizes developing robust control strategies using various approaches, including reinforcement learning (often incorporating force/torque and visual feedback), supervised learning with force/torque data, and visual servoing techniques, often leveraging deep neural networks for perception and control. These advancements aim to improve the speed, accuracy, and adaptability of robotic assembly tasks, impacting manufacturing automation and other fields requiring precise manipulation.