103 citations · 277 across the 16 of their papers we have counts for
16 papers
Reactive Stepping for Humanoid Robots using Reinforcement Learning: Application to Standing Push Recovery on the Exoskeleton Atalante
Alexis Duburcq, Fabian Schramm, Guilhem Boéris +2
State-of-the-art reinforcement learning is now able to learn versatile locomotion, balancing and push-recovery capabilities for bipedal robots in simulation. Yet, the reality gap h…
Morphological computation and decentralized learning in a swarm of sterically interacting robots
Matan Yah Ben Zion, Jeremy Fersula, Nicolas Bredeche +1
Whereas naturally occurring swarms thrive when crowded, physical interactions in robotic swarms are either avoided or carefully controlled, thus limiting their operational density.…
Meta-control of social learning strategies
Anil Yaman, Nicolas Bredeche, Onur Çaylak +2
Social learning, copying other's behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which in…
Policy Search with Rare Significant Events: Choosing the Right Partner to Cooperate with
Paul Ecoffet, Nicolas Fontbonne, Jean-Baptiste André +1
This paper focuses on a class of reinforcement learning problems where significant events are rare and limited to a single positive reward per episode. A typical example is that of…
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics
Stephane Doncieux, Nicolas Bredeche, Léni Le Goff +9
Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithm…
Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante
Alexis Duburcq, Yann Chevaleyre, Nicolas Bredeche +1
Autonomous robots require online trajectory planning capability to operate in the real world. Efficient offline trajectory planning methods already exist, but are computationally d…