103 citations · 120 across the 17 of their papers we have counts for
4 papers · 1 filter
Diminishing Return of Value Expansion Methods
Daniel Palenicek, Michael Lutter, João Carvalho +3
Model-based reinforcement learning aims to increase sample efficiency, but the accuracy of dynamics models and the resulting compounding errors are often seen as key limitations. T…
Analysing the Interplay of Vision and Touch for Dexterous Insertion Tasks
Janis Lenz, Theo Gruner, Daniel Palenicek +2
Robotic insertion tasks remain challenging due to uncertainties in perception and the need for precise control, particularly in unstructured environments. While humans seamlessly c…
Learning Tactile Insertion in the Real World
Daniel Palenicek, Theo Gruner, Tim Schneider +5
Humans have exceptional tactile sensing capabilities, which they can leverage to solve challenging, partially observable tasks that cannot be solved from visual observation alone.…
Iterated -Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning
Théo Vincent, Daniel Palenicek, Boris Belousov +2
The vast majority of Reinforcement Learning methods is largely impacted by the computation effort and data requirements needed to obtain effective estimates of action-value functio…