The field of dexterous manipulation is moving towards more generalizable and data-efficient approaches. Researchers are exploring new algorithms and frameworks that can handle diverse object shapes, deformations, and environments. A key trend is the use of object affordance maps and relative pose prediction to improve task success rates and reduce manual intervention. Another area of focus is the development of hierarchical pipelines that enable efficient policy training and execution. These innovations have the potential to enhance sample efficiency, learning generalizable manipulation policies, and robust performance in real-world applications. Noteworthy papers include: DexGarmentLab, which proposes a hierarchical garment-manipulation policy that outperforms existing methods, and EasyInsert, which demonstrates strong zero-shot generalization capability for unseen objects in cluttered environments. DORA is also notable for its object affordance-guided reinforcement learning framework that improves task success rates, while Object-Focus Actor achieves robust performance with only 10 demonstrations.