9 citations · 9 across the 2 of their papers we have counts for
16 papers
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…