153 citations · 557 across the 25 of their papers we have counts for
26 papers
Adaptive Noise Covariance Estimation under Colored Noise using Dynamic Expectation Maximization
Ajith Anil Meera, Pablo Lanillos
The accurate estimation of the noise covariance matrix (NCM) in a dynamic system is critical for state estimation and control, as it has a major influence in their optimality. Alth…
World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges
Tadahiro Taniguchi, Shingo Murata, Masahiro Suzuki +8
Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and develo…
Closed-form control with spike coding networks
Filip S. Slijkhuis, Sander W. Keemink, Pablo Lanillos
Efficient and robust control using spiking neural networks (SNNs) is still an open problem. Whilst behaviour of biological agents is produced through sparse and irregular spiking p…
Learning Policies for Continuous Control via Transition Models
Justus Huebotter, Serge Thill, Marcel van Gerven +1
It is doubtful that animals have perfect inverse models of their limbs (e.g., what muscle contraction must be applied to every joint to reach a particular location in space). Howev…
Object-based active inference
Ruben S. van Bergen, Pablo L. Lanillos
The world consists of objects: distinct entities possessing independent properties and dynamics. For agents to interact with the world intelligently, they must translate sensory in…
The Role of Valence and Meta-awareness in Mirror Self-recognition Using Hierarchical Active Inference
Jonathan Bauermeister, Pablo Lanillos
The underlying processes that enable self-perception are crucial for understanding multisensory integration, body perception and action, and the development of the self. Previous c…
Reclaiming saliency: rhythmic precision-modulated action and perception
Ajith Anil Meera, Filip Novicky, Thomas Parr +3
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 informatio…
Adaptation through prediction: multisensory active inference torque control
Cristian Meo, Giovanni Franzese, Corrado Pezzato +2
Adaptation to external and internal changes is major for robotic systems in uncertain environments. Here we present a novel multisensory active inference torque controller for indu…
Active Inference in Robotics and Artificial Agents: Survey and Challenges
Pablo Lanillos, Cristian Meo, Corrado Pezzato +8
Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning. Recently, it ha…
Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization
Justus F. Hübotter, Pablo Lanillos, Jakub M. Tomczak
Spiking neural networks are a promising approach towards next-generation models of the brain in computational neuroscience. Moreover, compared to classic artificial neural networks…
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem
Niels van Hoeffelen, Pablo Lanillos
Despite the potential of active inference for visual-based control, learning the model and the preferences (priors) while interacting with the environment is challenging. Here, we…
Robot Localization and Navigation through Predictive Processing using LiDAR
Daniel Burghardt, Pablo Lanillos
Knowing the position of the robot in the world is crucial for navigation. Nowadays, Bayesian filters, such as Kalman and particle-based, are standard approaches in mobile robotics.…
Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception
Pablo Lanillos, Marcel van Gerven
Unlike robots, humans learn, adapt and perceive their bodies by interacting with the world. Discovering how the brain represents the body and generates actions is of major importan…
Multimodal VAE Active Inference Controller
Cristian Meo, Pablo Lanillos
Active inference, a theoretical construct inspired by brain processing, is a promising alternative to control artificial agents. However, current methods do not yet scale to high-d…
Robot in the mirror: toward an embodied computational model of mirror self-recognition
Matej Hoffmann, Shengzhi Wang, Vojtech Outrata +2
Self-recognition or self-awareness is a capacity attributed typically only to humans and few other species. The definitions of these concepts vary and little is known about the mec…
Deep Active Inference for Partially Observable MDPs
Otto van der Himst, Pablo Lanillos
Deep active inference has been proposed as a scalable approach to perception and action that deals with large policy and state spaces. However, current models are limited to fully…
A deep active inference model of the rubber-hand illusion
Thomas Rood, Marcel van Gerven, Pablo Lanillos
Understanding how perception and action deal with sensorimotor conflicts, such as the rubber-hand illusion (RHI), is essential to understand how the body adapts to uncertain situat…
Robot self/other distinction: active inference meets neural networks learning in a mirror
Pablo Lanillos, Jordi Pages, Gordon Cheng
Self/other distinction and self-recognition are important skills for interacting with the world, as it allows humans to differentiate own actions from others and be self-aware. How…
End-to-End Pixel-Based Deep Active Inference for Body Perception and Action
Cansu Sancaktar, Marcel van Gerven, Pablo Lanillos
We present a pixel-based deep active inference algorithm (PixelAI) inspired by human body perception and action. Our algorithm combines the free-energy principle from neuroscience,…
Tactile Hallucinations on Artificial Skin Induced by Homeostasis in a Deep Boltzmann Machine
Michael Deistler, Yagmur Yener, Florian Bergner +2
Perceptual hallucinations are present in neurological and psychiatric disorders and amputees. While the hallucinations can be drug-induced, it has been described that they can even…
A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disorder
Pablo Lanillos, Daniel Oliva, Anja Philippsen +3
This survey presents the most relevant neural network models of autism spectrum disorder and schizophrenia, from the first connectionist models to recent deep network architectures…
Active inference body perception and action for humanoid robots
Guillermo Oliver, Pablo Lanillos, Gordon Cheng
Providing artificial agents with the same computational models of biological systems is a way to understand how intelligent behaviours may emerge. We present an active inference bo…
Sensorimotor learning for artificial body perception
German Diez-Valencia, Takuya Ohashi, Pablo Lanillos +1
Artificial self-perception is the machine ability to perceive its own body, i.e., the mastery of modal and intermodal contingencies of performing an action with a specific sensors/…
Attention-based Active Visual Search for Mobile Robots
Amir Rasouli, Pablo Lanillos, Gordon Cheng +1
We present an active visual search model for finding objects in unknown environments. The proposed algorithm guides the robot towards the sought object using the relevant stimuli p…