18 citations · 57 across the 10 of their papers we have counts for
12 papers
Efficient entity-based reinforcement learning
Vince Jankovics, Michael Garcia Ortiz, Eduardo Alonso
Recent deep reinforcement learning (DRL) successes rely on end-to-end learning from fixed-size observational inputs (e.g. image, state-variables). However, many challenging and int…
On the Sensory Commutativity of Action Sequences for Embodied Agents
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
Perception of artificial agents is one the grand challenges of AI research. Deep Learning and data-driven approaches are successful on constrained problems where perception can be…
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
Alban Laflaquière, Michael Garcia Ortiz
Despite its omnipresence in robotics application, the nature of spatial knowledge and the mechanisms that underlie its emergence in autonomous agents are still poorly understood. R…
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
Finding a generally accepted formal definition of a disentangled representation in the context of an agent behaving in an environment is an important challenge towards the construc…
S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
We consider the problem of building a state representation model for control, in a continual learning setting. As the environment changes, the aim is to efficiently compress the se…
Generative Models from the perspective of Continual Learning
Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz +2
Which generative model is the most suitable for Continual Learning? This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks. We…
State representation learning with recurrent capsule networks
Louis Annabi, Michael Garcia Ortiz
Unsupervised learning of compact and relevant state representations has been proved very useful at solving complex reinforcement learning tasks. In this paper, we propose a recurre…
Grounding Perception: A Developmental Approach to Sensorimotor Contingencies
Alban Laflaquière, Nikolas Hemion, Michaël Garcia Ortiz +1
Sensorimotor contingency theory offers a promising account of the nature of perception, a topic rarely addressed in the robotics community. We propose a developmental framework to…
Continual State Representation Learning for Reinforcement Learning using Generative Replay
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
We consider the problem of building a state representation model in a continual fashion. As the environment changes, the aim is to efficiently compress the sensory state's informat…
Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning
Hugo Caselles-Dupré, Louis Annabi, Oksana Hagen +2
Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents. It is of lower complexity compared to similar 3D platforms (e.g. De…
Learning Representations of Spatial Displacement through Sensorimotor Prediction
Michael Garcia Ortiz, Alban Laflaquière
Robots act in their environment through sequences of continuous motor commands. Because of the dimensionality of the motor space, as well as the infinite possible combinations of s…
Representation Learning in Partially Observable Environments using Sensorimotor Prediction
Thibaut Kulak, Michael Garcia Ortiz
In order to explore and act autonomously in an environment, an agent needs to learn from the sensorimotor information that is captured while acting. By extracting the regularities…