Learning from Demonstration (LfD) algorithms enable humans to teach new skills to robots through demonstrations. The learned skills can be robustly reproduced from the identical or near boundary conditions (e.g., initial point). However, when generalizing a learned skill over boundary conditions with higher variance, the similarity of the reproductions changes from one boundary condition to another, and a single LfD representation cannot preserve a consistent similarity across a generalization region. We propose a novel similarity-aware framework including multiple LfD representations and a similarity metric that can improve skill generalization by finding reproductions with the highest similarity values for a given boundary condition. Given a demonstration of the skill, our framework constructs a similarity region around a point of interest (e.g., initial point) by evaluating individual LfD representations using the similarity metric. Any point within this volume corresponds to a representation that reproduces the skill with the greatest similarity. We validate our multi-representational framework in three simulated and four sets of real-world experiments using a physical 6-DOF robot. We also evaluate 11 different similarity metrics and categorize them according to their biases in 286 simulated experiments.

翻译：从演示(LfD)算法学习后,人类能够通过演示向机器人传授新的技能。所学的技能可以从相同或接近边界条件(例如初始点)中强有力地复制。然而,如果在边界条件上普遍推广学习技能,差异较大,复制情况从一个边界条件变化到另一个边界条件的相似性,以及单一的LfD代表法不能在整个通用区域中保持一致的相似性。我们提出一个新的类似性认知框架,包括多个LfD代表法和类似性衡量标准,通过在特定边界条件中找到具有最高相似值的复制品,可以提高技能的通用性。鉴于技能的展示,我们的框架围绕一个利益点(例如初始点)构建了一个相似的区域,利用相似性度度度度度指标评估单个LfD代表的相似性。本卷内的任何点都相当于在最相似的区域复制技能的表示性。我们用一个物理的6-DOF机器人在三套模拟和四套现实世界实验中验证我们的多代表性框架。我们还评估了11种不同的类似性衡量标准,并将它们分类为286的模拟的偏差试验。