古典ロボティクス生成データセットによるロボットスキル学習：利点・欠点と今後の改善
Robot Skill Learning Via Classical Robotics-Based Generated Datasets: Advantages, Disadvantages, and Future Improvement
古典的なロボティクスアルゴリズムで生成したデータセットをロボットスキル学習に活用する利点と欠点を実験的に検討し、今後の改善方向を示した。
著者: Batu Kaan Oezen
分類: cs.RO, cs.AI, cs.CV, cs.IR, cs.SY, eess.SY
原文アブストラクト
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.