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.

中文翻译：

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通过基于经典机器人技术的生成数据集学习机器人技能：优点、缺点和未来改进

为什么我们不从我们长期存在的经典机器人知识中获益，而是寻找一些替代的数据收集方式？忽略所有现有方法的情况可能是一种浪费。本文认为，使用经典机器人算法创建的数据集是未来发展的重要组成部分。这种开发的经典算法具有完美的领域适应性和泛化性，最重要的是，基于它们收集数据集非常容易。众所周知，当前的机器人技能学习方法在看不见的领域表现异常糟糕，只要它们没有非常独特的大数据集，它们对抗对抗性攻击的性能就非常有限。我们的实验是使用由经典机器人代码创建的数据集的初始步骤。我们的实验研究了基于经典机器人技术的可能轨迹收集。它解决了一些优点和缺点，并指出了其他未来的发展思路。