<p>Multi-task reinforcement learning aims to enable agents to solve a family of tasks by leveraging shared knowledge, including generalization to previously unseen tasks. However, existing approaches remain limited by how knowledge is represented and shared across tasks, which restricts systematic reuse and undermines zero-shot generalization. In particular, skill-based methods learn reusable behavioral primitives, yet these skills are typically encoded as numerical latent representations without explicit semantic grounding, making it difficult to reason about their applicability to novel tasks. Motivated by the need for representations that support both reuse and interpretability, we introduce Semantic Skill Discovery (SemSD), a representation learning framework that grounds behavioral skills in human-interpretable task semantics to support transferable and compositional decision-making. SemSD aligns learned behavioral primitives with task descriptions in a shared embedding space and organizes skills according to semantic relationships, enabling principled skill selection and composition when encountering unseen tasks. Extensive experiments on the Meta-World benchmark demonstrate that SemSD achieves substantially improved zero-shot generalization performance across diverse tasks, with ablation studies further validating the importance of semantic grounding in multi-task skill learning.</p>