10 citations · 22 across the 4 of their papers we have counts for
5 papers
Parametric-Task MAP-Elites
Timothée Anne, Jean-Baptiste Mouret
Optimizing a set of functions simultaneously by leveraging their similarity is called multi-task optimization. Current black-box multi-task algorithms only solve a finite set of ta…
Multi-Task Multi-Behavior MAP-Elites
Anne, Mouret
We propose Multi-Task Multi-Behavior MAP-Elites, a variant of MAP-Elites that finds a large number of high-quality solutions for a large set of tasks (optimization problems from a…
First do not fall: learning to exploit a wall with a damaged humanoid robot
Timothée Anne, Eloïse Dalin, Ivan Bergonzani +2
Humanoid robots could replace humans in hazardous situations but most of such situations are equally dangerous for them, which means that they have a high chance of being damaged a…
Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments
Timothée Anne, Jack Wilkinson, Zhibin Li
This work developed a meta-learning approach that adapts the control policy on the fly to different changing conditions for robust locomotion. The proposed method constantly update…
Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors
Rituraj Kaushik, Timothée Anne, Jean-Baptiste Mouret
Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL) algorithms by finding an initial set of parameters for the dynamical model such that the model…