9 citations · 30 across the 13 of their papers we have counts for
7 papers · 1 filter
Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control
Achkan Salehi, Stephane Doncieux
Model-based RL/control have gained significant traction in robotics. Yet, these approaches often remain data-inefficient and lack the explainability of hand-engineered solutions. T…
E2R: a Hierarchical-Learning inspired Novelty-Search method to generate diverse repertoires of grasping trajectories
Johann Huber, Oumar Sane, Alex Coninx +2
Robotics grasping refers to the task of making a robotic system pick an object by applying forces and torques on its surface. Despite the recent advances in data-driven approaches,…
Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search
Aurélien Morel, Yakumo Kunimoto, Alex Coninx +1
Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector. No generic and autonomous method does exist to generate…
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…
Building an Affordances Map with Interactive Perception
Leni K. Le Goff, Oussama Yaakoubi, Alexandre Coninx +1
Robots need to understand their environment to perform their task. If it is possible to pre-program a visual scene analysis process in closed environments, robots operating in an o…
Bootstrapping Robotic Ecological Perception from a Limited Set of Hypotheses Through Interactive Perception
Léni K. Le Goff, Ghanim Mukhtar, Alexandre Coninx +1
To solve its task, a robot needs to have the ability to interpret its perceptions. In vision, this interpretation is particularly difficult and relies on the understanding of the s…