Searched for: subject%3A%22free%255C+energy%22
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Timmer, Sebastiaan (author)
<div>As Neuroscience progresses, there is an increasing amount of research that endorses predictions and reducing of prediction errors as one of the main functions of the brain. active inference is a brain-inspired, mathematical framework that successfully implements this idea both in simulations as well as in robotics. The predictive nature of...
master thesis 2024
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Dawe, Alon (author)
master thesis 2024
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Pezzato, C. (author)
In an ever-evolving society, the demand for autonomous robots equipped with human-level capabilities is becoming increasingly imperative. Various factors, such as an aging population and a shortage of labor for repetitive and physically demanding tasks, have underscored the need for capable autonomous robots to assist us in our daily activities....
doctoral thesis 2024
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Poulis, J.A. (author), Kwakernaak, A. (author)
The surface treatment of plastics as well as metals or ceramics includes a thorough surface cleaning as an essential step prior to adhesive bonding and coating processes. Besides this, surface activation of polymers is often needed because their surface free energy is too low for durable adhesion of a coating or adhesive. In this chapter...
book chapter 2024
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Dziarnowska, Weronika (author)
Researchers have been interested in studying the connection between emotion and memory for decades but much remains unknown due to the elusive nature of the human brain. Furthering our understanding of the phenomenon is crucial for improving the treatment of neurological disorders associated with emotion dysregulation, as well as for enhancing...
master thesis 2023
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Anil Meera, A. (author)
The potential impact of a grand unified theory of the brain on the robotics community might be immense, as it might hold the key to the general artificial intelligence. Such a theory might make revolutionary leaps in robot intelligence by improving the quality of our lives. The last two decades have witnessed the rise of one such brain theory -...
doctoral thesis 2023
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Pezzato, C. (author), Hernández, Carlos (author), Bonhof, S.D. (author), Wisse, M. (author)
In this article, we propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments, showing how robotic tasks can be formulated as a free-energy minimization problem. The proposed approach allows handling partially observable initial states and improves the...
journal article 2023
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Anil Meera, A. (author), Novicky, Filip (author), Parr, Thomas (author), Friston, Karl (author), Lanillos, Pablo (author), Sajid, Noor (author)
Computational models of visual attention in artificial intelligence and robotics have been inspired by the concept of a saliency map. These models account for the mutual information between the (current) visual information and its estimated causes. However, they fail to consider the circular causality between perception and action. In other...
journal article 2022
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Anil Meera, A. (author), Wisse, M. (author)
The free energy principle from neuroscience provides an efficient data-driven framework called the Dynamic Expectation Maximization (DEM), to learn the generative model in the environment. DEM’s growing potential to be the brain-inspired learning algorithm for robots demands a mathematically rigorous analysis using the standard control system...
conference paper 2022
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Deken, Mitchel (author)
Active inference is a novel brain theory based on the free energy principle, stating that every organism, in order to stay alive, minimizes a certain free energy. This theory is being translated into robot control, hoping to mimic the capabilities of the brain. Research in this field of robotics is still quite young, and active inference has yet...
master thesis 2021
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Coehoorn, Jesse (author)
The Free Energy Principle, which underlies Active Inference (AI), is a way to explain human perception and behaviour. Previous literature has hinted at a relation between AI and Linear-Quadratic Gaussian (LQG) control, the latter being a textbook controller. AI and LQG are, however, defined with different settings in mind: LQG has access to...
master thesis 2021
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Pancham, Naresh (author)
Active inference is a neuroscientific theory, which states that all living systems (e.g. the human brain) minimize a quantity termed the free energy. By minimizing this free energy, living systems keep an accurate representation of the world in their internal model (learning), are provided with an optimal way of acting on the world (action...
master thesis 2021
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van Vucht, Victor (author)
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...
master thesis 2021
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Baioumy, Mohamed (author), Pezzato, C. (author), Ferrari, Riccardo M.G. (author), Hernández, Carlos (author), Hawes, Nick (author)
This work presents a novel fault-tolerant control scheme based on active inference. Specifically, a new formulation of active inference which, unlike previous solutions, provides unbiased state estimation and simplifies the definition of probabilistically robust thresholds for fault-tolerant control of robotic systems using the free-energy. The...
conference paper 2021
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Anil Meera, A. (author), Wisse, M. (author)
The free energy principle from neuroscience has recently gained traction as one of the most prominent brain theories that can emulate the brain’s perception and action in a bio-inspired manner. This renders the theory with the potential to hold the key for general artificial intelligence. Leveraging this potential, this paper aims to bridge the...
journal article 2021
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Golchin, A. (author), Vardon, P.J. (author), Hicks, M.A. (author), Coombs, William M. (author), Pantev, I.A. (author)
The numerical implementation of a recently developed thermomechanical constitutive model for fine-grained soils based on hyperelasticity-hyperplasticity theory (Golchin et al. 2020), is presented. A new unconventional implicit stress return mapping algorithm, compatible with elasticity derived from Gibbs (complementary) energy potential, in...
conference paper 2021
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Benders, Dennis (author)
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,...
master thesis 2020
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Hijne, Iris (author)
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...
master thesis 2020
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van Roessel, L. (author)
Active inference is a process theory arising from neuroscience which casts perception, action, planning and learning under one optimisation criterion: minimisation of free energy. Current literature on the implementation of discrete state-space active inference focuses on scalability, the comparison to reinforcement learning and its performance...
master thesis 2020
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Rahbari, A. (author)
Improving and developing simulation techniques are key to obtaining higher efficiency and accuracy in molecular simulations of dense liquid systems. The methodology development introduced in this thesis is relevant both for academia and industrial applications. In this thesis, the methods developments/improvements for molecular simulations are...
doctoral thesis 2020
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