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20192022
most citedSparse Reward Exploration via Novelty Search and Emitters

9 citations · 25 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.RO2022

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…

cs.RO2019

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…

cs.RO20192 cited

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…

cs.RO20193 cited

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…

cs.RO2019

From exploration to control: learning object manipulation skills through novelty search and local adaptation

Seungsu Kim, Alexandre Coninx, Stephane Doncieux

Programming a robot to deal with open-ended tasks remains a challenge, in particular if the robot has to manipulate objects. Launching, grasping, pushing or any other object intera…