The field of robotics is witnessing significant advancements in manipulation and navigation capabilities, driven by the integration of large language models (LLMs) and vision-language-action models. Researchers are exploring innovative approaches to enable robots to execute complex tasks, adapt to dynamic environments, and interact with humans effectively. Notable developments include the use of LLMs for real-time task planning, execution, and feedback, as well as the incorporation of visual chain-of-thought reasoning and multi-agent collaboration. These advancements have led to improved performance, generalization, and adaptability in robotic systems. Some noteworthy papers in this area include: DAHLIA, which introduces a data-agnostic framework for language-conditioned long-horizon robotic manipulation, leveraging LLMs for real-time task planning and execution. REMAC, which proposes an adaptive multi-agent planning framework for efficient, scene-agnostic multi-robot long-horizon task planning and execution. GenSwarm, which presents an end-to-end system for automatically generating and deploying control policies for multi-robot tasks based on simple user instructions in natural language.