46 citations · 87 across the 13 of their papers we have counts for
16 papers
Theory of Mind Using Active Inference: A Framework for Multi-Agent Cooperation
Riddhi J. Pitliya, Ozan Çatal, Toon Van de Maele +2
Theory of Mind (ToM) -- the ability to understand that others can have differing knowledge and goals -- enables agents to reason about others' beliefs while planning their own acti…
Mobile Manipulation with Active Inference for Long-Horizon Rearrangement Tasks
Corrado Pezzato, Ozan Çatal, Toon Van de Maele +2
Despite growing interest in active inference for robotic control, its application to complex, long-horizon tasks remains untested. We address this gap by introducing a fully hierar…
AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models
Conor Heins, Toon Van de Maele, Alexander Tschantz +11
Current deep reinforcement learning (DRL) approaches achieve state-of-the-art performance in various domains, but struggle with data efficiency compared to human learning, which le…
Variational Bayes Gaussian Splatting
Toon Van de Maele, Ozan Catal, Alexander Tschantz +2
Recently, 3D Gaussian Splatting has emerged as a promising approach for modeling 3D scenes using mixtures of Gaussians. The predominant optimization method for these models relies…
Belief sharing: a blessing or a curse
Ozan Catal, Toon Van de Maele, Riddhi J. Pitliya +3
When collaborating with multiple parties, communicating relevant information is of utmost importance to efficiently completing the tasks at hand. Under active inference, communicat…
FMCW Radar Sensing for Indoor Drones Using Learned Representations
Ali Safa, Tim Verbelen, Ozan Catal +4
Frequency-modulated continuous-wave (FMCW) radar is a promising sensor technology for indoor drones as it provides range, angular as well as Doppler-velocity information about obst…
The Free Energy Principle for Perception and Action: A Deep Learning Perspective
Pietro Mazzaglia, Tim Verbelen, Ozan Çatal +1
The free energy principle, and its corollary active inference, constitute a bio-inspired theory that assumes biological agents act to remain in a restricted set of preferred states…
Disentangling What and Where for 3D Object-Centric Representations Through Active Inference
Toon Van de Maele, Tim Verbelen, Ozan Catal +1
Although modern object detection and classification models achieve high accuracy, these are typically constrained in advance on a fixed train set and are therefore not flexible to…
Towards bio-inspired unsupervised representation learning for indoor aerial navigation
Ni Wang, Ozan Catal, Tim Verbelen +2
Aerial navigation in GPS-denied, indoor environments, is still an open challenge. Drones can perceive the environment from a richer set of viewpoints, while having more stringent c…
LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping
Ozan Çatal, Wouter Jansen, Tim Verbelen +2
Biologically inspired algorithms for simultaneous localization and mapping (SLAM) such as RatSLAM have been shown to yield effective and robust robot navigation in both indoor and…
A learning gap between neuroscience and reinforcement learning
Samuel T. Wauthier, Pietro Mazzaglia, Ozan Çatal +3
Historically, artificial intelligence has drawn much inspiration from neuroscience to fuel advances in the field. However, current progress in reinforcement learning is largely foc…
Curiosity-Driven Exploration via Latent Bayesian Surprise
Pietro Mazzaglia, Ozan Catal, Tim Verbelen +1
The human intrinsic desire to pursue knowledge, also known as curiosity, is considered essential in the process of skill acquisition. With the aid of artificial curiosity, we could…
Deep Active Inference for Autonomous Robot Navigation
Ozan Çatal, Samuel Wauthier, Tim Verbelen +2
Active inference is a theory that underpins the way biological agent's perceive and act in the real world. At its core, active inference is based on the principle that the brain is…
Learning Perception and Planning with Deep Active Inference
Ozan Çatal, Tim Verbelen, Johannes Nauta +2
Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy. However, current experiment…
Learning to Catch Piglets in Flight
Ozan Çatal, Lawrence De Mol, Tim Verbelen +1
Catching objects in-flight is an outstanding challenge in robotics. In this paper, we present a closed-loop control system fusing data from two sensor modalities: an RGB-D camera a…
Bayesian policy selection using active inference
Ozan Çatal, Johannes Nauta, Tim Verbelen +2
Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task. Reinforcement Learning (RL) is a we…