Hierarchical Active Inference for Continuous Robot Control: Goal-Directed Hierarchical Models for Active Inference Agents

Active inference is a method for state estimation and control actions that is based on the Free Energy principle, which explains how biological agents infer the state of their environment and act upon it by maintaining a model of that environment and evaluating predictions. This method merges both action and sensory processing and is therefore a...

AR.Drone 2.0 state estimation using Dynamic Expectation Maximization: Bringing brain perception theory to practice

Recent developments in neuroscience research, mainly introduced by neuroscientist Karl J. Friston, have resulted in a concept called the Free Energy Principle (FEP). The FEP is a brain theory unifying action, perception and learning. An important observation is that autonomous robots have to perform similar tasks to the human brain. Therefore,...

Generalised Motions in Active Inference by finite differences: Active Inference in Robotics

This thesis is a contribution to the research on Active Inference for Robotics. Active Inference is an intricate, intriguing theory from neuroscience, a field in which it has already gained a greater following and popularity. This theory, based on the underlying Free Energy Principle, provides a unified account of perception, action and learning...