Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning
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 ...
| Authors | Shuaike Zhang, Shaokun Wang, Haoyu Tang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 June 2026 |
| Last updated at source | 28 September 2026 |
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