Peg in Hole Assembly

Robotic peg-in-hole assembly, a fundamental challenge in robotics, aims to develop robust and adaptable methods for precisely inserting a peg into a hole, even with uncertainties in position, orientation, and object properties. Current research focuses on leveraging reinforcement learning, particularly meta-reinforcement learning, and supervised learning approaches, often incorporating force/torque sensing and visual feedback, to improve accuracy and adaptability across diverse scenarios. These advancements utilize model architectures such as probabilistic dynamic movement primitives and neural networks for trajectory generation and control, aiming to achieve high success rates even with unseen object shapes and large initial misalignments. The resulting improvements in assembly efficiency and robustness have significant implications for manufacturing automation and space robotics.