Imagine telling your home robot to "make dinner" and it actually happens. That's the dream of robotic task planning, and researchers are getting closer with intriguing new techniques. One of the biggest hurdles is bridging the gap between human language and a robot's concrete actions. Large language models (LLMs) like GPT-4 excel at understanding our instructions, but they lack the real-world grounding to translate "make dinner" into a sequence of executable steps like chopping vegetables, boiling water, and so on. A new research paper proposes a clever solution: combining the power of LLMs with a formal reasoning system called Answer Set Programming (ASP). This hybrid approach, dubbed CLMASP, lets LLMs draft high-level plans while ASP fills in the practical details based on the robot’s abilities and the environment. Think of it as the LLM outlining the plot of a cooking show, and ASP writing the actual recipe with precise steps. In tests on a virtual home environment, CLMASP dramatically improved the success rate of robot task plans. While LLMs alone struggled to generate even basic executable plans, the LLM+ASP combo boosted the success rate to over 90%. This suggests that formal reasoning methods like ASP can provide the essential "common sense" that LLMs currently lack, allowing robots to translate vague instructions into real action. The study demonstrates the potential of combining the strengths of different AI approaches. While challenges remain in automating certain aspects of ASP and handling complex real-world environments, this research opens exciting new avenues for more intelligent, responsive home robots.