SkillPlug：用于机器人操作中少样本适应的无监督技能挖掘
SkillPlug: Unsupervised Skill Mining for Few-Shot Adaptation in Robotic Manipulation
AI总结：
针对机器人操作中学习可转移视觉运动模仿策略的挑战，提出SkillPlug框架，通过技能调节模块挖掘共享可转移技能库，经自监督学习形成任务共享先验，微调轻量级组件实现少样本适应，提升多任务和少样本适应性能。
摘要：
学习可转移的视觉运动模仿策略具有挑战性，现代策略多端到端训练，缺乏跨任务重用行为的结构。我们提出SkillPlug框架，通过技能调节模块增强现有视觉运动策略，从原始多任务演示中挖掘共享可转移技能库。通过自监督目标学习技能，形成任务共享先验。技能挖掘后微调轻量级路由器和动作头实现少样本适应。实验表明该方法能提升多任务和少样本适应性能。
英文摘要：
Learning transferable visuomotor imitation policies that generalize across diverse manipulation tasks and adapt rapidly to new tasks from only a handful of demonstrations remains challenging. Most modern policies are trained end-to-end to map observations directly to low-level actions, offering little explicit structure for reusing and recombining behaviors across tasks and making transfer data-inefficient under limited supervision. We propose SkillPlug, a plug-in framework that augments an existing visuomotor policy with a skill-conditioning module and mines a shared, transferable skill library from raw multi-task demonstrations. SkillPlug learns skills via self-supervised objectives that promote compact, reusable, and non-redundant behavior-level primitives, forming a task-shared prior for compositional control. After skill mining, we keep the learned skills fixed and specialize to unseen tasks by fine-tuning only lightweight router and action head, enabling efficient adaptation without full end-to-end retraining. We evaluate SkillPlug on two simulation benchmarks and on a real robot, and observe that the mined transferable skills consistently improve both multi-task performance and few-shot adaptation. Overall, SkillPlug offers a scalable way to mine reusable skills that improve data-efficient generalization in robotic manipulation.
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。
