A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

TL;DR

This paper offers a practical, structured roadmap for integrating Learning from Demonstration into manufacturing robotic manipulators, focusing on key questions and actionable steps for practitioners to develop customizable solutions.

Contribution

It provides a comprehensive, questionnaire-based guide tailored for manufacturing, addressing key challenges and strategies for effective LfD deployment in industrial robotics.

Findings

Guides practitioners through key steps from problem definition to solution refinement.

Addresses manufacturing-specific challenges and strategies for LfD performance enhancement.

Offers actionable insights for both researchers and industry professionals.

Abstract

This paper provides a structured and practical roadmap for practitioners to integrate Learning from Demonstration (LfD ) into manufacturing tasks, with a specific focus on industrial manipulators. Motivated by the paradigm shift from mass production to mass customization, it is crucial to have an easy-to-follow roadmap for practitioners with moderate expertise, to transform existing robotic processes to customizable LfD-based solutions. To realize this transformation, we devise the key questions of "What to Demonstrate", "How to Demonstrate", "How to Learn", and "How to Refine". To follow through these questions, our comprehensive guide offers a questionnaire-style approach, highlighting key steps from problem definition to solution refinement. The paper equips both researchers and industry professionals with actionable insights to deploy LfD-based solutions effectively. By tailoring the refinement criteria to manufacturing settings, the paper addresses related challenges and strategies for enhancing LfD performance in manufacturing contexts.