Peg in Hole Task

The peg-in-hole task, a fundamental challenge in robotics, focuses on precisely inserting a peg into a hole with minimal clearance, often under conditions of uncertainty. Current research emphasizes robust control strategies, leveraging models like Gaussian processes, belief space search, and deep neural networks trained via reinforcement learning or learning from demonstrations, to address challenges such as partial observability, varying hole geometries, and environmental uncertainties. These advancements are crucial for automating assembly tasks in manufacturing, surgery, and other fields requiring precise manipulation in complex or unpredictable environments, improving efficiency and reducing human error.