Learning from Demonstration (LfD) aims to encode versatile skills from human demonstrations. The field has been gaining popularity since it facilitates knowledge transfer to robots without requiring expert knowledge in robotics. 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 in reference frames, enabling better skill generalization to new situations. However, most TP-LfD algorithms require multiple demonstrations in various environment 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 concept for learning motion policies from few demonstrations by finding the reference frame weights which capture frame importance/relevance during task executions. Experimental results in both simulation and real robotic environments validate our approach.

翻译：示教学习旨在从人类示教中编码通用技能。该领域因无需机器人专业知识即可实现知识向机器人迁移而日益流行。在执行任务过程中，机器人运动通常受到环境约束的影响。为此，任务参数化示教学习将相关上下文信息编码至参考坐标系中，从而提升技能在新场景下的泛化能力。然而，大多数任务参数化示教学习算法需要在多种环境条件下获取多次示教，以确保模型具有足够的统计显著性。要求机器人用户创建不同场景并在所有场景下完成示教并非易事。因此，本文提出一种新概念——通过计算表征任务执行过程中坐标系重要性/相关性的帧权重，实现从少量示教中学习运动策略。仿真与实际机器人环境的实验结果验证了本方法的有效性。