Insertion Task

Robotic insertion tasks, focusing on precisely inserting objects into tight spaces, are a crucial area of robotics research aiming to improve automation in manufacturing and other fields. Current research emphasizes learning-based approaches, employing techniques like reinforcement learning, diffusion models, and visual servoing, often incorporating multi-modal sensing (visual, tactile, force) to handle uncertainties and improve robustness. These advancements are significant because successful solutions will enable more dexterous and adaptable robots capable of performing complex assembly tasks in unstructured environments, reducing reliance on human intervention and increasing efficiency.