Robot Skill Learning Via Classical Robotics-Based Generated Datasets: Advantages, Disadvantages, and Future Improvement

TL;DR

This paper explores using datasets generated by classical robotics algorithms for robot skill learning, highlighting their advantages, limitations, and potential for future improvements in domain adaptation and generalization.

Contribution

It introduces the concept of leveraging classical robotics algorithms for dataset creation to enhance robot skill learning, emphasizing their domain adaptation and ease of data collection.

Findings

Classical robotics algorithms produce datasets with strong domain adaptation.

Using such datasets can improve robot skill learning in unseen domains.

The approach has limitations that suggest directions for future research.

Abstract

Why do we not profit from our long-existing classical robotics knowledge and look for some alternative way for data collection? The situation ignoring all existing methods might be such a waste. This article argues that a dataset created using a classical robotics algorithm is a crucial part of future development. This developed classic algorithm has a perfect domain adaptation and generalization property, and most importantly, collecting datasets based on them is quite easy. It is well known that current robot skill-learning approaches perform exceptionally badly in the unseen domain, and their performance against adversarial attacks is quite limited as long as they do not have a very exclusive big dataset. Our experiment is the initial steps of using a dataset created by classical robotics codes. Our experiment investigated possible trajectory collection based on classical robotics. It addressed some advantages and disadvantages and pointed out other future development ideas.