The field of robotics is witnessing a significant shift towards leveraging artificial intelligence (AI) to improve task performance. Recent developments have focused on integrating AI-generated images, language models, and neuro-symbolic learning to enhance robot control and decision-making. Researchers are exploring the use of generative AI systems to implicitly encode knowledge about the world, allowing robots to make informed decisions in under-specified tasks. Additionally, language models are being utilized to guide robots in out-of-distribution recovery, option discovery, and autonomous generation of sub-goals. These advancements have the potential to significantly improve the efficiency and adaptability of robots in complex environments. Noteworthy papers include:

• World Knowledge from AI Image Generation for Robot Control, which investigates using AI-generated images to solve under-specified tasks.
• Leveraging Language Models for Out-of-Distribution Recovery in Reinforcement Learning, which introduces a novel approach for recovery learning using language models.
• Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning, which proposes a framework for learning abstract representations of tasks and skills.