评估家用机器人上的持续学习
Evaluating Continual Learning on a Home Robot
AI总结：
本文面向低成本家用机器人提出 SANER 持续学习技能库方法，并以 ABIP 为骨干，在无任务 ID、每任务仅少量演示条件下学习四个连续厨房任务。
摘要：
家庭环境中的机器人需要能够随着数据变得可用而持续学习新技能，从而随着时间推移变得更加强大，同时使用尽可能少的真实世界数据。然而，传统机器人学习方法通常假设存在大量独立同分布（iid）数据，这与上述目标并不一致。相比之下，CLEAR 和 SANE 等持续学习方法允许自主智能体基于非 iid 样本流进行学习；但此前它们尚未在真实机器人平台上得到演示。在本工作中，我们展示如何将持续学习方法改造并用于一台真实的低成本家用机器人，并特别考察在无任务 ID 的设置下示例数量极少的情况。具体而言，我们提出 SANER，这是一种用于持续学习技能库的方法，并提出 ABIP（Attention-Based Interaction Policies，基于注意力的交互策略）作为支持该方法的骨干网络。我们在一台低成本家用机器人上学习四个连续的厨房任务，每个任务仅使用少量演示。
英文摘要：
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.
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。
