Incremental Learning of Motor Skills Through Demonstrations

In this survey, we explore the concept of learning from demonstrations (LfD) in the context of robotics. LfD is a framework that enables robots to learn new skills and adapt them to unseen situations by observing human demonstrations. We examine the different approaches to LfD, including imitation learning [2], incremental learning [5], and movement primitive-based learning [3].
To illustrate how LfD works, consider a robot that wants to learn how to make a cup of tea. The robot can observe a human expert making the tea and use this demonstration to learn the sequence of movements involved. By breaking down the movement sequence into smaller parts or "movement primitives," the robot can then practice the skills it has learned from the demonstration, and adapt them to new situations.
There are several benefits to using LfD in robotics. Firstly, it allows robots to learn new skills without explicit programming, making it easier to train robots for complex tasks. Secondly, LfD enables robots to adapt their behavior to unseen situations, such as when a human demonstrator makes a mistake or deviates from the expected movement pattern. Finally, LfD can be used in a variety of robotics applications, including household tasks, elderly care, and collaborative workspaces.
Despite its potential, there are also challenges associated with LfD. One of the main difficulties is dealing with noisy or incomplete demonstrations, which can lead to errors in learning. Additionally, LfD can be computationally expensive, especially when dealing with complex tasks or large amounts of data. To overcome these challenges, researchers are exploring new techniques such as transfer learning [7], multi-modal sensing [8], and unsupervised learning methods [9].
In summary, learning from demonstrations is a powerful framework for training robots to perform complex tasks. By breaking down movements into smaller parts, LfD enables robots to learn new skills and adapt them to unseen situations. While there are challenges associated with LfD, ongoing research is addressing these issues and exploring new techniques to improve the efficiency and accuracy of LfD in robotics.