23 citations · 24 across the 4 of their papers we have counts for
8 papers
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…
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…