Complex Manipulation

Complex robotic manipulation research aims to enable robots to perform intricate tasks involving dexterous interaction with objects, focusing on improving efficiency and robustness. Current efforts leverage imitation learning, often combined with reinforcement learning techniques like soft actor-critic and Bayesian optimization, to learn policies from limited demonstrations, sometimes employing curriculum learning or behavior primitives to decompose complex tasks. These advancements are significant because they reduce the data requirements for training, enabling more efficient and cost-effective development of robots capable of performing a wider range of manipulation tasks in real-world settings.