23 citations · 62 across the 13 of their papers we have counts for
19 papers
Discovering and Exploiting Sparse Rewards in a Learned Behavior Space
Giuseppe Paolo, Miranda Coninx, Alban Laflaquière +1
Learning optimal policies in sparse rewards settings is difficult as the learning agent has little to no feedback on the quality of its actions. In these situations, a good strateg…
Sparse Reward Exploration via Novelty Search and Emitters
Giuseppe Paolo, Alexandre Coninx, Stephane Doncieux +1
Reward-based optimization algorithms require both exploration, to find rewards, and exploitation, to maximize performance. The need for efficient exploration is even more significa…
Emergence of Spatial Coordinates via Exploration
Alban Laflaquière
Spatial knowledge is a fundamental building block for the development of advanced perceptive and cognitive abilities. Traditionally, in robotics, the Euclidean (x,y,z) coordinate s…
Novelty Search makes Evolvability Inevitable
Stephane Doncieux, Giuseppe Paolo, Alban Laflaquière +1
Evolvability is an important feature that impacts the ability of evolutionary processes to find interesting novel solutions and to deal with changing conditions of the problem to s…
Unsupervised Learning and Exploration of Reachable Outcome Space
Giuseppe Paolo, Alban Laflaquière, Alexandre Coninx +1
Performing Reinforcement Learning in sparse rewards settings, with very little prior knowledge, is a challenging problem since there is no signal to properly guide the learning pro…
Self-supervised Body Image Acquisition Using a Deep Neural Network for Sensorimotor Prediction
Alban Laflaquière, Verena V. Hafner
This work investigates how a naive agent can acquire its own body image in a self-supervised way, based on the predictability of its sensorimotor experience. Our working hypothesis…
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…
Unsupervised Emergence of 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…
Identification of Invariant Sensorimotor Structures as a Prerequisite for the Discovery of Objects
Nicolas Le Hir, Olivier Sigaud, Alban Laflaquière
Perceiving the surrounding environment in terms of objects is useful for any general purpose intelligent agent. In this paper, we investigate a fundamental mechanism making object…
Learning agent's spatial configuration from sensorimotor invariants
Alban Laflaquière, J. Kevin O'Regan, Sylvain Argentieri +2
The design of robotic systems is largely dictated by our purely human intuition about how we perceive the world. This intuition has been proven incorrect with regard to a number of…
Grounding the Experience of a Visual Field through Sensorimotor Contingencies
Alban Laflaquière
Artificial perception is traditionally handled by hand-designing task specific algorithms. However, a truly autonomous robot should develop perceptive abilities on its own, by inte…
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…
A Non-linear Approach to Space Dimension Perception by a Naive Agent
Alban Laflaquière, Sylvain Argentieri, Olivia Breysse +2
Developmental Robotics offers a new approach to numerous AI features that are often taken as granted. Traditionally, perception is supposed to be an inherent capacity of the agent.…
Learning an internal representation of the end-effector configuration space
Alban Laflaquière, Alexander V. Terekhov, Bruno Gas +1
Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or en…
Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience
Alban Laflaquière, J. Kevin O'Regan, Bruno Gas +1
In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective. Despite its pervasive nature in…
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…
A Sensorimotor Perspective on Grounding the Semantic of Simple Visual Features
Alban Laflaquière
In Machine Learning and Robotics, the semantic content of visual features is usually provided to the system by a human who interprets its content. On the contrary, strictly unsuper…
Grounding object perception in a naive agent's sensorimotor experience
Alban Laflaquière, Nikolas Hemion
Artificial object perception usually relies on a priori defined models and feature extraction algorithms. We study how the concept of object can be grounded in the sensorimotor exp…
Autonomous Grounding of Visual Field Experience through Sensorimotor Prediction
Alban Laflaquière
In a developmental framework, autonomous robots need to explore the world and learn how to interact with it. Without an a priori model of the system, this opens the challenging pro…