Learning from Demonstration (LfD) enables robots to acquire versatile skills by learning motion policies from human demonstrations. It endows users with an intuitive interface to transfer new skills to robots without the need for time-consuming robot programming and inefficient solution exploration. During task executions, the robot motion is usually influenced by constraints imposed by environments. In light of this, task-parameterized LfD (TP-LfD) encodes relevant contextual information into reference frames, enabling better skill generalization to new situations. However, most TP-LfD algorithms typically require multiple demonstrations across various environmental conditions to ensure sufficient statistics for a meaningful model. It is not a trivial task for robot users to create different situations and perform demonstrations under all of them. Therefore, this paper presents a novel algorithm to learn skills from few demonstrations. By leveraging the reference frame weights that capture the frame importance or relevance during task executions, our method demonstrates excellent skill acquisition performance, which is validated in real robotic environments.

翻译：从演示中学习（LfD）使机器人能够通过从人类演示中学习运动策略来获取多技能。它为用户提供直观的接口，无需耗时的人工编程和低效的解空间探索即可将新技能迁移至机器人。任务执行过程中，机器人运动通常受环境约束影响。因此，任务参数化演示学习（TP-LfD）将相关上下文信息编码至参考坐标系，实现技能在新场景下的泛化。然而，现有TP-LfD算法通常需要多种环境条件下的多次演示，以获取足够统计量构建有效模型。对于机器人用户而言，设计不同场景并完成所有场景下的演示并非易事。本文提出一种基于少量演示的技能学习算法，通过利用表征任务执行过程中坐标系重要性/相关性的帧权重，该方法在真实机器人环境中展现出卓越的技能获取性能。