research
∙
06/14/2023
VIBR: Learning View-Invariant Value Functions for Robust Visual Control
End-to-end reinforcement learning on images showed significant progress ...
share
research
∙
07/20/2022
Latent Discriminant deterministic Uncertainty
Predictive uncertainty estimation is essential for deploying Deep Neural...
research
∙
03/02/2022
MUAD: Multiple Uncertainties for Autonomous Driving benchmark for multiple uncertainty types and tasks
Predictive uncertainty estimation is essential for deploying Deep Neural...
research
∙
02/17/2022
A study of deep perceptual metrics for image quality assessment
Several metrics exist to quantify the similarity between images, but the...
research
∙
09/17/2021
Efficient State Representation Learning for Dynamic Robotic Scenarios
While the rapid progress of deep learning fuels end-to-end reinforcement...
research
∙
07/05/2021
Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?
Affordances are the possibilities of actions the environment offers to t...
research
∙
07/05/2021
SCOD: Active Object Detection for Embodied Agents using Sensory Commutativity of Action Sequences
We introduce SCOD (Sensory Commutativity Object Detection), an active me...
research
∙
05/20/2021
Evaluating Robustness over High Level Driving Instruction for Autonomous Driving
In recent years, we have witnessed increasingly high performance in the ...
research
∙
04/24/2021
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
The latest Deep Learning (DL) models for detection and classification ha...
research
∙
04/02/2021
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
Most of state of the art methods applied on time series consist of deep ...
research
∙
05/13/2020
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics
Robots are still limited to controlled conditions, that the robot design...
research
∙
02/13/2020
On the Sensory Commutativity of Action Sequences for Embodied Agents
We study perception in the scenario of an embodied agent equipped with f...
research
∙
12/06/2019
Regularization Shortcomings for Continual Learning
In classical machine learning, the data streamed to the algorithms is as...
research
∙
07/11/2019
DisCoRL: Continual Reinforcement Learning via Policy Distillation
In multi-task reinforcement learning there are two main challenges: at t...
research
∙
06/29/2019
Continual Learning for Robotics
Continual learning (CL) is a particular machine learning paradigm where ...
research
∙
06/11/2019
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer
We focus on the problem of teaching a robot to solve tasks presented seq...
research
∙
03/30/2019
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
Finding a generally accepted formal definition of a disentangled represe...
research
∙
02/25/2019
S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
We consider the problem of building a state representation model for con...
research
∙
01/24/2019
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
Scaling end-to-end reinforcement learning to control real robots from vi...
research
∙
12/21/2018
Generative Models from the perspective of Continual Learning
Which generative model is the most suitable for Continual Learning? This...
research
∙
10/31/2018
Don't forget, there is more than forgetting: new metrics for Continual Learning
Continual learning consists of algorithms that learn from a stream of da...
research
∙
10/29/2018
Marginal Replay vs Conditional Replay for Continual Learning
We present a new replay-based method of continual classification learnin...
research
∙
10/09/2018
Continual State Representation Learning for Reinforcement Learning using Generative Replay
We consider the problem of building a state representation model in a co...
research
∙
09/25/2018
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
State representation learning aims at learning compact representations f...
research
∙
09/12/2018
Learning structure-from-motionfrom motion
This work is based on a questioning of the quality metrics used by deep ...
research
∙
09/12/2018
Multi range Real-time depth inference from a monocular stabilized footage using a Fully Convolutional Neural Network
Using a neural network architecture for depth map inference from monocul...
research
∙
09/12/2018
End-to-end depth from motion with stabilized monocular videos
We propose a depth map inference system from monocular videos based on a...
research
∙
09/03/2018
Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning
We propose Flatland, a simple, lightweight environment for fast prototyp...
research
∙
06/28/2018
Training Discriminative Models to Evaluate Generative Ones
Generative models are known to be difficult to assess. Recent works, esp...
research
∙
04/02/2018
Exploring to learn visual saliency: The RL-IAC approach
The problem of object localization and recognition on autonomous mobile ...
research
∙
02/12/2018
State Representation Learning for Control: An Overview
Representation learning algorithms are designed to learn abstract featur...
research
∙
09/15/2017