# Autonomous learning and chaining of motor primitives using the Free Energy Principle

arXiv: https://arxiv.org/abs/2005.05151 · HTML: https://mlsanity.com/paper/2005.05151/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 2 of 2)

- [Bidirectional Interaction between Visual and Motor Generative Models using Predictive Coding and Active Inference](https://mlsanity.com/paper/2104.09163/) (2021)
- [Modulating Reservoir Dynamics via Reinforcement Learning for Efficient Robot Skill Synthesis](https://mlsanity.com/paper/2411.10991/) (2024)

## Abstract (arXiv)

In this article, we apply the Free-Energy Principle to the question of motor primitives learning. An echo-state network is used to generate motor trajectories. We combine this network with a perception module and a controller that can influence its dynamics. This new compound network permits the autonomous learning of a repertoire of motor trajectories. To evaluate the repertoires built with our method, we exploit them in a handwriting task where primitives are chained to produce long-range sequences.