Gated networks: an inventory

Gated networks are networks that contain gating connections, in which the outputs of at least two neurons are multiplied. Initially, gated networks were used to learn relationships between two input sources, such as pixels from two images. More recently, they have been applied to learning activity recognition or multi-modal representations. The aims of this paper are threefold: 1) to explain the basic computations in gated networks to the non-expert, while adopting a standpoint that insists on their symmetric nature. 2) to serve as a quick reference guide to the recent literature, by providing an inventory of applications of these networks, as well as recent extensions to the basic architecture. 3) to suggest future research directions and applications.Comment: Unpublished manuscript, 17 page

Unsupervised state representation learning with robotic priors: a robustness benchmark

Our understanding of the world depends highly on our capacity to produce intuitive and simplified representations which can be easily used to solve problems. We reproduce this simplification process using a neural network to build a low dimensional state representation of the world from images acquired by a robot. As in Jonschkowski et al. 2015, we learn in an unsupervised way using prior knowledge about the world as loss functions called robotic priors and extend this approach to high dimension richer images to learn a 3D representation of the hand position of a robot from RGB images. We propose a quantitative evaluation of the learned representation using nearest neighbors in the state space that allows to assess its quality and show both the potential and limitations of robotic priors in realistic environments. We augment image size, add distractors and domain randomization, all crucial components to achieve transfer learning to real robots. Finally, we also contribute a new prior to improve the robustness of the representation. The applications of such low dimensional state representation range from easing reinforcement learning (RL) and knowledge transfer across tasks, to facilitating learning from raw data with more efficient and compact high level representations. The results show that the robotic prior approach is able to extract high level representation as the 3D position of an arm and organize it into a compact and coherent space of states in a challenging dataset.Comment: ICRA 2018 submissio

Manuel d'éducation des jeunes robots à l'usage de leurs maitres

Article de vulgarisation publié dans la revue "La Jaune et la Rouge"La robotique développementale s'inspire des études de biologie et de psychologie du développement humain pour jeter les bases de nos futurs robots. Domestiques, assistants, ils devront être capables de percevoir et d'interpréter une immense variété d'objets et de situations. Au cours de leur existence, ils apprendront au contact de leur maître des tâches nouvelles et de plus en plus complexes. Il parait ainsi difficile de les doter de toutes les connaissances nécessaires dès leur sortie d'usine. La solution idéale ne serait-elle donc pas de les doter de capacités d'apprentissage et de les éduquer

Topological segmentation of indoors/outdoors sequences of spherical views

International audienceTopological navigation consists for a robot in navigating in a topological graph which nodes are topological places. Either for indoor or outdoor environments, segmen- tation into topological places is a challenging issue. In this paper, we propose a common approach for indoor and out- door environment segmentation without elaborating a complete topological navigation system. The approach is novel in that environment sensing is performed using spherical images. Envi- ronment structure estimation is performed by a global structure descriptor specially adapted to the spherical representation. This descriptor is processed by a custom designed algorithm which detects change-points defining the segmentation between topological places