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Using Tactile Sensing to Improve the Sample Efficiency and Performance of Deep Deterministic Policy Gradients for Simulated In-Hand Manipulation Tasks

Deep Reinforcement Learning techniques demonstrate advances in the domain of robotics. One of the limiting factors is a large number of interaction samples usually required for training in simulated and real-world environments. In this work, we demonstrate for a set of simulated dexterous in-hand ob...

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Vydáno v:Front Robot AI
Hlavní autoři: Melnik, Andrew, Lach, Luca, Plappert, Matthias, Korthals, Timo, Haschke, Robert, Ritter, Helge
Médium: Artigo
Jazyk:Inglês
Vydáno: Frontiers Media S.A. 2021
Témata:
On-line přístup:https://ncbi.nlm.nih.gov/pmc/articles/PMC8275974/
https://ncbi.nlm.nih.gov/pubmed/34268337
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/frobt.2021.538773
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