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

arXiv: https://arxiv.org/abs/2301.08794 · HTML: https://mlsanity.com/paper/2301.08794/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Large-Scale Study of Curiosity-Driven Learning](https://mlsanity.com/paper/1808.04355/) (2018)
- [Do CIFAR-10 Classifiers Generalize to CIFAR-10?](https://mlsanity.com/paper/1806.00451/) (2018)
- [One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning](https://mlsanity.com/paper/1802.01557/) (2018)
- [Human-in-the-Loop Imitation Learning using Remote Teleoperation](https://mlsanity.com/paper/2012.06733/) (2020)
- [When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?](https://mlsanity.com/paper/2204.05618/) (2022)
- [Transformers for One-Shot Visual Imitation](https://mlsanity.com/paper/2011.05970/) (2020)
- [Specializing Versatile Skill Libraries using Local Mixture of Experts](https://mlsanity.com/paper/2112.04216/) (2021)
- [ROS Navigation Tuning Guide](https://mlsanity.com/paper/1706.09068/) (2017)
- [Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility](https://mlsanity.com/paper/2112.06442/) (2021)
- [Sensorimotor representation learning for an "active self" in robots: A model survey](https://mlsanity.com/paper/2011.12860/) (2020)

## Abstract (arXiv)

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.