  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.
