Similarity-Aware Skill Reproduction based on Multi-Representational Learning from Demonstration

TL;DR

This paper introduces a similarity-aware multi-representational learning framework for Learning from Demonstration, enhancing skill generalization by selecting the most similar reproduction across boundary conditions using multiple representations and similarity metrics.

Contribution

It proposes a novel framework that combines multiple LfD representations with a similarity metric to improve skill reproduction consistency over varied boundary conditions.

Findings

The framework improves skill generalization in robot demonstrations.

Evaluation of 11 similarity metrics reveals their biases and effectiveness.

Successful validation in simulated and real-world robot experiments.

Abstract

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.