Complex Robotic Task

Complex robotic task execution is a rapidly advancing field aiming to enable robots to perform intricate, multi-step actions autonomously. Current research focuses on improving learning efficiency through techniques like curriculum learning (often guided by large language models), hierarchical reinforcement learning, and imitation learning, often incorporating model-based and model-free approaches alongside advanced vision and tactile sensing. These advancements are crucial for bridging the gap between simulated and real-world robotic performance, ultimately leading to more robust and adaptable robots for industrial automation, assistive technologies, and other applications.