Imagine a robot learning to assemble furniture, not from pre-programmed instructions, but by understanding a simple explanation of the task, like a human would. Researchers are exploring this idea with a new approach called Plan-Seq-Learn (PSL). This method combines the strengths of three different AI techniques: language models, motion planning, and reinforcement learning. First, a large language model (LLM) interprets the task description and creates a high-level plan, breaking down the task into smaller steps. Then, a motion planning system uses visual information to guide the robot's movements, ensuring it reaches the right locations to perform each step. Finally, reinforcement learning (RL) allows the robot to learn the fine-grained control skills needed to interact with objects, like grasping a tool or turning a knob. This modular approach allows the robot to learn complex tasks efficiently, even with sparse rewards or obstacles in the environment. PSL has shown promising results in simulated environments, solving tasks with up to 10 steps, like assembling nuts and bolts or using kitchen appliances. While still in its early stages, this research suggests a future where robots can learn to perform complex tasks from simple instructions, opening up new possibilities for automation and human-robot collaboration. Challenges remain, such as handling dynamic environments and improving the robustness of the system to noisy sensor data. However, PSL represents a significant step towards creating more adaptable and intelligent robots.