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：示教学习算法使人类能够通过演示向机器人传授新技能。习得的技能可以从相同或接近的边界条件（例如起始点）稳健地复现。然而，当在具有更高方差的边界条件下泛化已习得技能时，复现的相似性会随边界条件的变化而改变，单一示教学习表征无法在整个泛化区域内保持一致的相似性。我们提出了一种新颖的相似性感知框架，包含多个示教学习表征及一种相似性度量，该框架可通过为给定边界条件寻找具有最高相似性值的复现来改进技能泛化。给定技能的演示后，本框架通过使用相似性度量评估各个示教学习表征，围绕兴趣点（例如起始点）构建相似性区域。该区域内的任意点均对应一个能以最大相似性复现该技能的表征。我们在三个仿真实验和四组使用实体六自由度机器人的真实世界实验中验证了所提出的多表征框架。我们还在286次仿真实验中评估了11种不同的相似性度量，并根据其偏差对其进行了分类。