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，使实体机器人能够在其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示范，从一系列新任务中构建一个不断增长的技能库。该算法首先利用开放词汇视觉模型进行持续技能发现，从未分割的示范中提取作为重复模式出现的技能。持续技能发现机制通过更新现有技能来避免对先前任务的灾难性遗忘，并新增技能以解决新任务。LOTUS训练了一个元控制器，可在终身学习过程中灵活组合多种技能，处理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。更多结果与视频可在项目网站查看：https://ut-austin-rpl.github.io/Lotus/。