The field of autonomous robotics is moving towards leveraging large language models (LLMs) and their variants to enhance path planning and decision-making capabilities. Researchers are exploring innovative approaches to integrate LLMs with traditional planning algorithms, enabling robots to interpret high-level semantic instructions and navigate complex environments. The use of LLMs as teacher models to train lightweight small language models is gaining traction, allowing for efficient deployment on edge devices. Additionally, the development of hierarchical planning frameworks and probabilistic program induction methods is improving the ability of robots to reason under uncertainty and adapt to dynamic scenarios. Noteworthy papers include: SmallPlan, which presents a novel framework for leveraging LLMs to train small language models for path planning tasks. Semantic Intelligence, which integrates GPT-4 with A* planning to enable low-cost robots to exhibit intelligent, context-aware behaviors. MORE, which enhances the capabilities of language models for zero-shot mobile manipulation planning. NAMO-LLM, which proposes a sampling-based planner guided by LLMs to compute feasible plans for navigating among movable obstacles.