16 citations · 27 across the 16 of their papers we have counts for
4 papers · 1 filter
Improving Robot Dual-System Motor Learning with Intrinsically Motivated Meta-Control and Latent-Space Experience Imagination
Muhammad Burhan Hafez, Cornelius Weber, Matthias Kerzel +1
Combining model-based and model-free learning systems has been shown to improve the sample efficiency of learning to perform complex robotic tasks. However, dual-system approaches…
Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space
Muhammad Burhan Hafez, Cornelius Weber, Matthias Kerzel +1
Combining model-based and model-free deep reinforcement learning has shown great promise for improving sample efficiency on complex control tasks while still retaining high perform…
Curious Meta-Controller: Adaptive Alternation between Model-Based and Model-Free Control in Deep Reinforcement Learning
Muhammad Burhan Hafez, Cornelius Weber, Matthias Kerzel +1
Recent success in deep reinforcement learning for continuous control has been dominated by model-free approaches which, unlike model-based approaches, do not suffer from representa…
Deep Intrinsically Motivated Continuous Actor-Critic for Efficient Robotic Visuomotor Skill Learning
Muhammad Burhan Hafez, Cornelius Weber, Matthias Kerzel +1
In this paper, we present a new intrinsically motivated actor-critic algorithm for learning continuous motor skills directly from raw visual input. Our neural architecture is compo…