Assembly Planning

Assembly planning focuses on automatically determining the optimal sequence of actions to assemble multiple parts into a complete structure, a crucial challenge in robotics and manufacturing. Current research heavily utilizes reinforcement learning, often coupled with graph-based representations or physics-aware simulations, to overcome the combinatorial complexity of finding feasible assembly sequences. Deep learning models, including transformers and normalizing flows, are employed to learn assembly policies from data, sometimes incorporating user preferences or leveraging disassembly for improved efficiency. These advancements promise to significantly improve automation in various sectors, from industrial manufacturing to furniture assembly and even space exploration.