The field of robotic assembly and manipulation is moving towards more sophisticated and autonomous systems. Recent developments have focused on improving the reliability and efficiency of robotic assembly tasks, particularly in complex scenarios involving multiple parts and connectors. Researchers are exploring the use of vision-language models to extract structured information from instruction manuals and generate plans for assembly tasks. Another area of research is the development of intent-driven planning pipelines that can construct action sequences for complex manipulation tasks, such as disassembly, using large language models and computer vision. Additionally, there is a growing interest in using reinforcement learning and vision-language models to solve long-horizon routing tasks involving deformable linear objects. Noteworthy papers include Manual2Skill++, which presents a vision-language framework for extracting connection information from assembly manuals, and Intent-Driven LLM Ensemble Planning, which proposes a pipeline for planning complex manipulation tasks using large language models. Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models is also notable for its fully autonomous hierarchical framework for solving DLO routing tasks. MemER is another significant contribution, which proposes a hierarchical policy framework for scaling up memory for robot control via experience retrieval.