We introduce LOTUS, a continual imitation learning algorithm that empowers a physical robot to continuously and efficiently learn to solve new manipulation tasks throughout its lifespan. The core idea behind LOTUS is constructing an ever-growing skill library from a sequence of new tasks with a small number of human demonstrations. LOTUS starts with a continual skill discovery process using an open-vocabulary vision model, which extracts skills as recurring patterns presented in unsegmented demonstrations. Continual skill discovery updates existing skills to avoid catastrophic forgetting of previous tasks and adds new skills to solve novel tasks. LOTUS trains a meta-controller that flexibly composes various skills to tackle vision-based manipulation tasks in the lifelong learning process. Our comprehensive experiments show that LOTUS outperforms state-of-the-art baselines by over 11% in success rate, showing its superior knowledge transfer ability compared to prior methods. More results and videos can be found on the project website: https://ut-austin-rpl.github.io/Lotus/.

翻译：我们提出LOTUS，一种持续模仿学习算法，使物理机器人能够在整个生命周期内持续高效地学习解决新操作任务。LOTUS的核心思想在于：通过少量人类示范，从一系列新任务中构建一个不断增长的技能库。算法首先利用开放词汇视觉模型进行持续技能发现，将未分段的示范数据中重复出现的模式提取为技能。持续技能发现机制在更新现有技能以避免灾难性遗忘的同时，新增技能以解决全新任务。LOTUS训练了一个元控制器，在终身学习过程中灵活组合多种技能应对基于视觉的操作任务。综合实验表明，LOTUS在成功率上比现有最优基线方法提升超过11%，展现出优于先前方法的知识迁移能力。更多结果与视频请访问项目网站：https://ut-austin-rpl.github.io/Lotus/。