The field of robot learning and manipulation is rapidly advancing, with a focus on developing more efficient and effective methods for training robots to perform complex tasks. One of the key directions in this area is the development of new reward structures and reinforcement learning algorithms that can handle long-horizon tasks and sparse rewards. Researchers are also exploring the use of visual and spatial information to improve robot manipulation, such as learning to anticipate and plan for future actions. Additionally, there is a growing interest in using temporal logic and stage-aware reward modeling to enable robots to perform complex tasks that involve multiple stages and sub-tasks. Notable papers in this area include ReLAM, which introduces a novel framework for automatically generating dense rewards from action-free video demonstrations, and TGPO, which proposes a hierarchical framework for solving general Signal Temporal Logic tasks. Other noteworthy papers include STAIR, which addresses stage misalignment in preference-based reinforcement learning, and TimeRewarder, which learns dense rewards from passive videos via frame-wise temporal distance.