通过可复用技能学习新任务：面向具身持续学习的技能组合专家
Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning
AI总结：
提出技能组合专家（SCE）框架，通过组合技能基础（CSG）分解演示为可复用技能，并利用双执行-转换专家（DETE）实现新任务学习，有效缓解具身持续学习中的灾难性遗忘。
摘要：
具身持续学习（ECL）旨在使机器人能够在闭环控制下持续获取新的操作任务，同时保留先前学习的行为。与传统的持续学习相比，ECL遭受更严重的灾难性遗忘。在闭环控制下累积的特征漂移通过顺序决策逐步传播，导致先前学习的行为退化。ECL中的一个关键挑战在于如何在不断演变的任务中进行结构化的技能复用，因为现有方法主要关注技能学习，而没有明确组织它们以执行连贯的任务。为了解决这个问题，我们提出了SCE，一个用于ECL的技能组合专家框架。SCE通过组合技能基础（CSG）构建技能库，将任务演示分解为可复用的技能。在此基础上，双执行-转换专家（DETE）通过技能组合实现新任务学习，其中一个分支确保技能执行，另一个支持技能之间的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持续提高了保留率和整体任务性能。进一步的特征漂移分析和消融研究验证了我们方法的有效性。项目网站：https://eqcy.github.io/sce/。
英文摘要：
Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behaviors under closed-loop control. In ECL, feature drift can propagate through sequential decision-making under closed-loop control, turning representation changes into compounding behavioral deviations on previously learned tasks. 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 show that SCE 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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
