56 citations · 64 across the 12 of their papers we have counts for
12 papers
Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration
Ajith Anil Meera, Pablo Lanillos, Wouter Kouw
An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map wh…
Robot Metacognition: Decision Making with Confidence for Tool Invention
Ajith Anil Meera, Poppy Collis, Polina Arbuzova +4
Robots today often miss a key ingredient of truly intelligent behavior: the ability to reflect on their own cognitive processes and decisions. In humans, this self-monitoring or me…
Designing Tools with Control Confidence
Ajith Anil Meera, Abian Torres, Pablo Lanillos
Prehistoric humans invented stone tools for specialized tasks by not just maximizing the tool's immediate goal-completion accuracy, but also increasing their confidence in the tool…
A theory of generalised coordinates for stochastic differential equations
Lancelot Da Costa, Nathaël Da Costa, Conor Heins +5
Stochastic differential equations are ubiquitous modelling tools in physics and the sciences. In most modelling scenarios, random fluctuations driving dynamics or motion have some…
Confidence-Aware Decision-Making and Control for Tool Selection
Ajith Anil Meera, Pablo Lanillos
Self-reflecting about our performance (e.g., how confident we are) before doing a task is essential for decision making, such as selecting the most suitable tool or choosing the be…
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…
Free Energy Principle for the Noise Smoothness Estimation of Linear Systems with Colored Noise
Ajith Anil Meera, Martijn Wisse
The free energy principle (FEP) from neuroscience provides a framework called active inference for the joint estimation and control of state space systems, subjected to colored noi…
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…
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…
Free Energy Principle for State and Input Estimation of a Quadcopter Flying in Wind
Fred Bos, Ajith Anil Meera, Dennis Benders +1
The free energy principle from neuroscience provides a brain-inspired perception scheme through a data-driven model learning algorithm called Dynamic Expectation Maximization (DEM)…
A Brain Inspired Learning Algorithm for the Perception of a Quadrotor in Wind
Ajith Anil Meera, Martijn Wisse
The quest for a brain-inspired learning algorithm for robots has culminated in the free energy principle from neuroscience that models the brain's perception and action as an optim…
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search
Ajith Anil Meera, Marija Popovic, Alexander Millane +1
Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal sear…