Imagine a robot that can understand and perform complex, multi-step tasks, not just simple pick-and-place operations. Researchers are tackling this challenge of "long-horizon" robot tasks, where robots need to execute a series of actions over an extended period. A new study introduces the Therblig-based Backbone Framework (TBBF), a novel approach to improve how robots understand and learn these complex tasks. The core idea is to break down large tasks into smaller, fundamental actions called "therbligs." Think of them like building blocks for robot behavior. These therbligs, combined with cutting-edge AI models, help robots grasp the context of a task and adapt to new situations more effectively. The researchers developed a two-stage system. First, an "offline training" phase teaches a neural network to recognize these therbligs within a demonstration. Second, in "online testing," the system analyzes a single demonstration of a new task and uses the learned therbligs to guide the robot. This process is further enhanced by "Action Registration," which links the therbligs to the objects in the robot’s environment, ensuring precise actions. Additionally, a "Large Language Model (LLM)" helps fine-tune the robot’s actions, correcting for minor errors and uncertainties. In tests, the system achieved a remarkable 94.4% success rate on new long-horizon tasks in simple scenarios and 80% in more complex, cluttered environments. This research represents a significant step towards more adaptable and intelligent robots capable of performing intricate tasks in the real world. Future work will focus on making the system even more robust, handling noisy demonstrations, and adapting it to a wider range of robot platforms. The ultimate goal is to create robots that can learn complex tasks quickly and efficiently, opening up new possibilities in various industries and applications.