10 citations · 16 across the 11 of their papers we have counts for
12 papers
Towards Robust Prehensile Manipulation in Open-Ended Environments
Mathilde Kappel, Mahdi Khoramshahi, Louis Annabi +2
We propose to use Quality-Diversity (QD) algorithms to solve robotic prehensile manipulation tasks in open-ended environments. Our approach enables the efficient discovery of a wid…
A Sim-to-Real Integration Pipeline for Training and Deployment of Chunk-Based VLA Manipulation Policies
Mathilde Kappel, Clémence Grislain, Mohamed Chetouani +5
Vision-Language-Action (VLA) models have become a prominent paradigm for mapping multimodal inputs, including semantic instructions, visual observations of the scene, and proprioce…
QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation
Mathilde Kappel, Mahdi Khoramshahi, Louis Annabi +2
Thanks to the latest advances in learning and robotics, domestic robots are beginning to enter homes, aiming to execute household chores autonomously. However, robots still struggl…
Disentangled Representation Learning through Unsupervised Symmetry Group Discovery
Barthélémy Dang-Nhu, Louis Annabi, Sylvain Argentieri
Symmetry-based disentangled representation learning leverages the group structure of environment transformations to uncover the latent factors of variation. Prior approaches to sym…
Prerequisite Structure Discovery in Intelligent Tutoring Systems
Louis Annabi, Sao Mai Nguyen
This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems. T…
On the Relationship Between Variational Inference and Auto-Associative Memory
Louis Annabi, Alexandre Pitti, Mathias Quoy
In this article, we propose a variational inference formulation of auto-associative memories, allowing us to combine perceptual inference and memory retrieval into the same mathema…
Intrinsically Motivated Learning of Causal World Models
Louis Annabi
Despite the recent progress in deep learning and reinforcement learning, transfer and generalization of skills learned on specific tasks is very limited compared to human (or anima…
A Predictive Coding Account for Chaotic Itinerancy
Louis Annabi, Alexandre Pitti, Mathias Quoy
As a phenomenon in dynamical systems allowing autonomous switching between stable behaviors, chaotic itinerancy has gained interest in neurorobotics research. In this study, we dra…
Bidirectional Interaction between Visual and Motor Generative Models using Predictive Coding and Active Inference
Louis Annabi, Alexandre Pitti, Mathias Quoy
In this work, we build upon the Active Inference (AIF) and Predictive Coding (PC) frameworks to propose a neural architecture comprising a generative model for sensory prediction,…
Autonomous learning and chaining of motor primitives using the Free Energy Principle
Louis Annabi, Alexandre Pitti, Mathias Quoy
In this article, we apply the Free-Energy Principle to the question of motor primitives learning. An echo-state network is used to generate motor trajectories. We combine this netw…
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…
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…