Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behaviors under closed-loop control. Compared with conventional continual learning, ECL suffers from more severe catastrophic forgetting. Feature drift accumulated under closed-loop control progressively propagates through sequential decision-making, leading to degradation of previously learned behaviors. A key challenge in ECL lies in structured skill reuse across continually evolving tasks, since existing methods primarily focus on skill learning without explicitly organizing them for coherent task execution. To address this issue, we propose SCE, a Skill-Compositional Experts framework for ECL. SCE builds a skill base via Compositional Skill Grounding (CSG), which decomposes task demonstrations into reusable skills. Based on this, Dual Execution-and-Transition Experts (DETE) enable new task learning through skill composition, where one branch ensures skill execution and the other supports transitions between skills for coherent behavior. Experiments on LIBERO benchmarks and real-world manipulation tasks demonstrate that SCE consistently improves retention and overall task performance. Further feature drift analyses and ablation studies verify the effectiveness of our method. Project website: https://eqcy.github.io/sce/.

翻译：具身持续学习（ECL）旨在使机器人在闭环控制下持续获取新操作任务的同时，保持已习得的行为能力。与传统持续学习相比，ECL面临着更严重的灾难性遗忘问题。闭环控制下积累的特征漂移会通过序贯决策过程逐步传播，导致先前学习的行为出现退化。ECL的一个关键挑战在于如何在持续演化的任务间实现结构化技能复用，因为现有方法主要关注技能学习本身，而未对技能进行显式组织以达成连贯的任务执行。为解决该问题，我们提出SCE——面向ECL的技能组合专家框架。SCE通过组合性技能基础（CSG）构建技能库，将任务示范分解为可复用技能。在此基础上，双重执行与转换专家（DETE）通过技能组合实现新任务学习，其中一支分支确保技能执行，另一支分支支持技能间转换以实现连贯行为。在LIBERO基准测试和真实世界操作任务上的实验表明，SCE能持续提升任务保持表现和整体任务性能。进一步的特征漂移分析与消融实验验证了方法的有效性。项目网站：https://eqcy.github.io/sce/。