Robots in home environments need to be able to learn new skills continuously as data becomes available, becoming ever more capable over time while using as little real-world data as possible. However, traditional robot learning approaches typically assume large amounts of iid data, which is inconsistent with this goal. In contrast, continual learning methods like CLEAR and SANE allow autonomous agents to learn off of a stream of non-iid samples; they, however, have not previously been demonstrated on real robotics platforms. In this work, we show how continual learning methods can be adapted for use on a real, low-cost home robot, and in particular look at the case where we have extremely small numbers of examples, in a task-id-free setting. Specifically, we propose SANER, a method for continuously learning a library of skills, and ABIP (Attention-Based Interaction Policies) as the backbone to support it. We learn four sequential kitchen tasks on a low-cost home robot, using only a handful of demonstrations per task.

翻译：家庭环境中的机器人需要能够在数据可用时持续学习新技能，随着时间的推移变得更为强大，同时尽可能少使用真实世界的数据。然而，传统的机器人学习方法通常假设有大量独立同分布的数据，这与这一目标不一致。相比之下，像CLEAR和SANE这样的持续学习方法允许自主代理从非独立同分布的样本流中学习；但这些方法此前未在真实机器人平台上得到验证。在这项工作中，我们展示了如何将持续学习方法适配到真实、低成本的家庭机器人上，并特别关注在任务身份未知的设置下，仅有极少量示例的情况。具体而言，我们提出了SANER（一种用于持续学习技能库的方法）以及ABIP（基于注意力的交互策略）作为支撑它的骨干网络。我们在低成本家庭机器人上学习了四项顺序执行的厨房任务，每项任务仅使用少量演示。