# Learning Skills from Demonstrations: A Trend from Motion Primitives to Experience Abstraction

arXiv: https://arxiv.org/abs/2210.08060 · HTML: https://mlsanity.com/paper/2210.08060/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2022.

## Built on by (most cited first, 5 of 5)

- [SPECI: Skill Prompts based Hierarchical Continual Imitation Learning for Robot Manipulation](https://mlsanity.com/paper/2504.15561/) (2025)
- [Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview](https://mlsanity.com/paper/2403.19916/) (2024)
- [Learning Multimodal Latent Dynamics for Human-Robot Interaction](https://mlsanity.com/paper/2311.16380/) (2023)
- [Human Centric General Physical Intelligence for Agile Manufacturing Automation](https://mlsanity.com/paper/2508.11960/) (2025)
- [Cognitive Manipulation: Semi-supervised Visual Representation and Classroom-to-real Reinforcement Learning for Assembly in Semi-structured Environments](https://mlsanity.com/paper/2406.00364/) (2024)

## Abstract (arXiv)

The uses of robots are changing from static environments in factories to encompass novel concepts such as Human-Robot Collaboration in unstructured settings. Pre-programming all the functionalities for robots becomes impractical, and hence, robots need to learn how to react to new events autonomously, just like humans. However, humans, unlike machines, are naturally skilled in responding to unexpected circumstances based on either experiences or observations. Hence, embedding such anthropoid behaviours into robots entails the development of neuro-cognitive models that emulate motor skills under a robot learning paradigm. Effective encoding of these skills is bound to the proper choice of tools and techniques. This paper studies different motion and behaviour learning methods ranging from Movement Primitives (MP) to Experience Abstraction (EA), applied to different robotic tasks. These methods are scrutinized and then experimentally benchmarked by reconstructing a standard pick-n-place task. Apart from providing a standard guideline for the selection of strategies and algorithms, this paper aims to draw a perspectives on their possible extensions and improvements