research
∙
09/18/2023
Learning Spatial and Temporal Hierarchies: Hierarchical Active Inference for navigation in Multi-Room Maze Environments
Cognitive maps play a crucial role in facilitating flexible behaviour by...
share
research
∙
08/30/2023
Learning to Navigate from Scratch using World Models and Curiosity: the Good, the Bad, and the Ugly
Learning to navigate unknown environments from scratch is a challenging ...
research
∙
08/16/2023
Integrating cognitive map learning and active inference for planning in ambiguous environments
Living organisms need to acquire both cognitive maps for learning the st...
research
∙
07/05/2023
FOCUS: Object-Centric World Models for Robotics Manipulation
Understanding the world in terms of objects and the possible interplays ...
research
∙
06/23/2023
Inferring Hierarchical Structure in Multi-Room Maze Environments
Cognitive maps play a crucial role in facilitating flexible behaviour by...
research
∙
06/08/2023
Active Inference in Hebbian Learning Networks
This work studies how brain-inspired neural ensembles equipped with loca...
research
∙
04/14/2023
Symmetry and Complexity in Object-Centric Deep Active Inference Models
Humans perceive and interact with hundreds of objects every day. In doin...
research
∙
02/07/2023
Object-Centric Scene Representations using Active Inference
Representing a scene and its constituent objects from raw sensory data i...
research
∙
01/06/2023
FMCW Radar Sensing for Indoor Drones Using Learned Representations
Frequency-modulated continuous-wave (FMCW) radar is a promising sensor t...
research
∙
11/23/2022
Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors...
research
∙
10/09/2022
Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning
This work proposes a first-of-its-kind SLAM architecture fusing an event...
research
∙
09/24/2022
Unsupervised Model-based Pre-training for Data-efficient Control from Pixels
Controlling artificial agents from visual sensory data is an arduous tas...
research
∙
09/16/2022
Disentangling Shape and Pose for Object-Centric Deep Active Inference Models
Active inference is a first principles approach for understanding the br...
research
∙
08/27/2022
Learning to SLAM on the Fly in Unknown Environments: A Continual Learning Approach for Drones in Visually Ambiguous Scenes
Learning to safely navigate in unknown environments is an important task...
research
∙
08/19/2022
Home Run: Finding Your Way Home by Imagining Trajectories
When studying unconstrained behaviour and allowing mice to leave their c...
research
∙
08/18/2022
Learning Generative Models for Active Inference using Tensor Networks
Active inference provides a general framework for behavior and learning ...
research
∙
07/13/2022
The Free Energy Principle for Perception and Action: A Deep Learning Perspective
The free energy principle, and its corollary active inference, constitut...
research
∙
10/19/2021
Contrastive Active Inference
Active inference is a unifying theory for perception and action resting ...
research
∙
09/28/2021
Fail-Safe Human Detection for Drones Using a Multi-Modal Curriculum Learning Approach
Drones are currently being explored for safety-critical applications whe...
research
∙
08/26/2021
Disentangling What and Where for 3D Object-Centric Representations Through Active Inference
Although modern object detection and classification models achieve high ...
research
∙
07/15/2021
A Low-Complexity Radar Detector Outperforming OS-CFAR for Indoor Drone Obstacle Avoidance
As radar sensors are being miniaturized, there is a growing interest for...
research
∙
06/17/2021
Towards bio-inspired unsupervised representation learning for indoor aerial navigation
Aerial navigation in GPS-denied, indoor environments, is still an open c...
research
∙
05/07/2021
LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping
Biologically inspired algorithms for simultaneous localization and mappi...
research
∙
04/22/2021
A learning gap between neuroscience and reinforcement learning
Historically, artificial intelligence has drawn much inspiration from ne...
research
∙
04/15/2021
Self-Supervised Exploration via Latent Bayesian Surprise
Training with Reinforcement Learning requires a reward function that is ...
research
∙
03/24/2020
Dynamic Narrowing of VAE Bottlenecks Using GECO and L_0 Regularization
When designing variational autoencoders (VAEs) or other types of latent ...
research
∙
03/06/2020
Deep Active Inference for Autonomous Robot Navigation
Active inference is a theory that underpins the way biological agent's p...
research
∙
02/21/2020
Rhythm, Chord and Melody Generation for Lead Sheets using Recurrent Neural Networks
Music that is generated by recurrent neural networks often lacks a sense...
research
∙
01/30/2020
Learning Perception and Planning with Deep Active Inference
Active inference is a process theory of the brain that states that all l...
research
∙
01/28/2020
Learning to Catch Piglets in Flight
Catching objects in-flight is an outstanding challenge in robotics. In t...
research
∙
12/20/2019
A Survey on Distributed Machine Learning
The demand for artificial intelligence has grown significantly over the ...
research
∙
06/25/2019
Software Engineering Practices for Machine Learning
In the last couple of years we have witnessed an enormous increase of ma...
research
∙
04/17/2019
Bayesian policy selection using active inference
Learning to take actions based on observations is a core requirement for...
research
∙
11/12/2018
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations
In this work we explore the generalization characteristics of unsupervis...
research
∙
09/11/2018
Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification
Because of their state-of-the-art performance in computer vision, CNNs a...
research
∙
06/09/2018
Learning to Grasp from a Single Demonstration
Learning-based approaches for robotic grasping using visual sensors typi...
research
∙
05/30/2018
Privacy Aware Offloading of Deep Neural Networks
Deep neural networks require large amounts of resources which makes them...
research
∙
11/29/2017
Transfer Learning with Binary Neural Networks
Previous work has shown that it is possible to train deep neural network...
research
∙
08/09/2017
Decoupled Learning of Environment Characteristics for Safe Exploration
Reinforcement learning is a proven technique for an agent to learn a tas...
research
∙
03/13/2017
Sensor Fusion for Robot Control through Deep Reinforcement Learning
Deep reinforcement learning is becoming increasingly popular for robot c...
research
∙
05/27/2016