Imagine instructing a robot to navigate a cluttered warehouse, picking specific items while avoiding obstacles and respecting safety zones. This level of complex planning, involving multiple constraints and long action sequences, has long been a challenge for robotics. Traditional methods require painstakingly detailed programming, while simpler AI approaches struggle with the nuances of real-world scenarios. Now, researchers are exploring how Large Language Models (LLMs), like those powering ChatGPT, can bridge this gap. A new framework called CaStL (Constraints as Specifications through LLM Translation) empowers LLMs to interpret complex natural language instructions and translate them into executable robot commands. This involves breaking down instructions into smaller, manageable constraints, such as "always avoid the red zone" or "pick up the blue box only after the green one." CaStL uses a multi-step process: First, it clarifies ambiguities in the natural language, ensuring the LLM understands the task's specifics. Then, it identifies and categorizes constraints, such as goal conditions, action ordering, and restricted actions. Finally, it translates these constraints into a format that robot planning algorithms can understand. This involves generating PDDL (Planning Domain Definition Language) code and Python scripts that interact with a constraint-aware task and motion planner. This allows the robot to consider not just the *what* but also the *how* of a task, accounting for physical limitations and environmental obstacles. Tested in simulated environments like navigating rooms with locked doors, assembling blocks, and making sandwiches in a kitchen, CaStL significantly improved the robot's success rate in completing complex tasks. However, challenges remain. Crafting effective prompts for the LLM and ensuring it correctly interprets constraints require expertise. The computational cost of using large language models can also be significant. Future research aims to address these limitations by exploring more efficient prompting techniques, smaller language models, and support for an even wider range of constraints, including temporal and geometric considerations. The ultimate goal is a future where we can easily instruct robots to perform complex, multi-step tasks through natural language, unlocking their full potential in various real-world applications.