Abstract: Dynamic unstructured construction environments are characterized by open sites, time-varying working conditions, discrete material properties, and complex interactions among humans, robots, and construction objects. These characteristics make it difficult for construction robots to directly follow the development path of industrial robots, which typically operate in fixed workstations with deterministic processes. To address the transition of construction robots from automatic execution of single tasks to autonomous operation in complex on-site environments, this paper focuses on skill learning methods for direct construction manipulation tasks. It reviews recent progress, task adaptation logic, and engineering bottlenecks of deep reinforcement learning, imitation learning, transfer learning, and multi-agent learning. The analysis shows that skill learning for construction robots has shifted from fixed program execution to feedback-based policy optimization, demonstration-driven skill acquisition, cross-scenario skill reuse, and multi-agent collaboration. Differentiated methodological pathways have gradually emerged in masonry and assembly, concrete construction, demolition and maintenance, and earthwork tasks. However, existing studies are generally still in the transitional stage from proof-of-concept validation to engineering deployment, facing common challenges such as low sample efficiency, difficult sim-to-real transfer, limited skill generalization, and insufficient long-term validation. Future research should focus on embodied theoretical modeling, few-shot safe learning, and multimodal state representation to support stable applications in complex construction sites.