The field of robotics and artificial intelligence is rapidly advancing, with a focus on developing more sophisticated and autonomous systems. Recent research has explored the use of foundation models to enhance robot perception and action, enabling more effective localization, interaction, and manipulation in unstructured environments. Additionally, there has been a growing interest in leveraging large language models (LLMs) to improve robotic capabilities, such as language-to-action systems and semantic path planning. These advancements have the potential to significantly improve the performance and autonomy of robotic systems, enabling them to operate more effectively in complex and dynamic environments. Noteworthy papers include: Leveraging Foundation Models for Enhancing Robot Perception and Action, which investigates the use of foundation models to enhance robotic capabilities. Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence, which presents a framework for heterogeneous robot teams to accomplish complex missions in unstructured environments. A Multi-Modal Neuro-Symbolic Approach for Spatial Reasoning-Based Visual Grounding in Robotics, which proposes a novel neuro-symbolic framework for spatial reasoning and visual grounding in robotics.