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

TL;DR

This paper reviews and benchmarks various robot learning methods from motion primitives to experience abstraction, aiming to improve autonomous response in unstructured environments.

Contribution

It provides a comparative analysis and benchmarking of different learning techniques for robotic skills, offering guidelines and perspectives for future enhancements.

Findings

Motion primitives effectively encode basic motor skills.

Experience abstraction captures complex behaviors and contextual information.

Benchmark results highlight strengths and limitations of each method.

Abstract

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