Robotic assembly is crucial across many industries, from automotive and aerospace to electronics, medical devices, and even everyday items like furniture and appliances. However, it remains one of the toughest challenges in robot learning due to the combined challenges of accurate perception, multi-modal sensing, affordance reasoning, long-horizon task and motion planning, grasping, contact-rich and dexterous manipulation, physical simulation, sim2real transfer, and generalization over arbitrary objects with complex shapes. Our workshop aims to gather experts to discuss how robot learning can tackle these challenges. Key topics include:

• Assembly planning: Learning to plan and optimize for safe, efficient, and feasible assembly sequences.
• Assembly skill learning: Learning skills such as grasping, in-hand manipulation, insertion, screwing, and tool usage to solve contact-rich and high-accuracy assembly tasks.
• Foundation models: Leveraging foundation models for generalizable perception, planning, and control for assembly.
• Multi-modal sensing: Integrating vision, force, and tactile sensing for improved precision and reliability for assembly.
• Datasets and benchmarks: New datasets or benchmarks on assembly tasks featuring task and object diversity, real-world complexity, multiple modalities and robot embodiments.
• Human-robot interaction: Learning assembly from demonstrations or language instructions, or collaborating with humans.
• Industry-level assembly: Filling the gap of employing robot learning to solve real-factory assembly problems with scale.