Long-Horizon Planning for Multi-Agent Robots

In the realm of autonomous robots, where the rizz of language models meets the edge of technology, we dive into the skibidi dance of multi-agent planning. Imagine a squad of robots, not just goons but savvy agents, navigating partially observable environments, utilizing the LLaMAR framework. This brainrot concept allows for a plan-act-correct-verify groove, where actions are fine-tuned without the need for fancy oracles. The MAP-THOR playground, a beta sigma alpha of household tasks, showcases LLaMAR's prowess, outperforming competitors by a solid 30%. This is not just a game; it is a revolution in how robots decode human intentions, transforming challenges into a smooth flow of collaboration. Get ready, the future is here.