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中持续高效地学习解决新的操作任务。LOTUS的核心思想是通过少量人类演示，从一系列新任务中构建一个持续增长的技能库。LOTUS首先利用开放词汇视觉模型进行持续技能发现，从非分段演示中提取作为重复模式呈现的技能。持续技能发现会更新已有技能以避免对先前任务的灾难性遗忘，同时添加新技能以解决新任务。LOTUS训练了一个元控制器，能够在终身学习过程中灵活组合多种技能来处理基于视觉的操作任务。我们的综合实验表明，LOTUS在成功率上比最先进的基线方法高出11%以上，展示了其相较于先前方法更优异的知识迁移能力。更多结果和视频可访问项目网站：https://ut-austin-rpl.github.io/Lotus/。