200 citations · 557 across the 42 of their papers we have counts for
9 papers · 1 filter
Spatial and Temporal Hierarchy for Autonomous Navigation using Active Inference in Minigrid Environment
Daria de Tinguy, Toon van de Maele, Tim Verbelen +1
Robust evidence suggests that humans explore their environment using a combination of topological landmarks and coarse-grained path integration. This approach relies on identifiabl…
Learning to Navigate from Scratch using World Models and Curiosity: the Good, the Bad, and the Ugly
Daria de Tinguy, Sven Remmery, Pietro Mazzaglia +2
Learning to navigate unknown environments from scratch is a challenging problem. This work presents a system that integrates world models with curiosity-driven exploration for auto…
FOCUS: Object-Centric World Models for Robotics Manipulation
Stefano Ferraro, Pietro Mazzaglia, Tim Verbelen +1
Understanding the world in terms of objects and the possible interplays with them is an important cognition ability, especially in robotics manipulation, where many tasks require r…
Object-Centric Scene Representations using Active Inference
Toon Van de Maele, Tim Verbelen, Pietro Mazzaglia +2
Representing a scene and its constituent objects from raw sensory data is a core ability for enabling robots to interact with their environment. In this paper, we propose a novel a…
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…
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…
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…
Sensor Fusion for Robot Control through Deep Reinforcement Learning
Steven Bohez, Tim Verbelen, Elias De Coninck +3
Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured s…