Robot Skill Learning Via Classical Robotics-Based Generated Datasets: Advantages, Disadvantages, and Future Improvement
This addresses data scarcity and generalization issues in robotics, but it is incremental as it builds on existing classical methods rather than introducing a new paradigm.
The paper tackles the problem of poor generalization and adversarial robustness in robot skill learning by proposing to use datasets generated via classical robotics algorithms, which offer easy collection and strong domain adaptation.
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.