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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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Bibliografiset tiedot
Julkaisussa:Front Robot AI
Päätekijät: Melnik, Andrew, Lach, Luca, Plappert, Matthias, Korthals, Timo, Haschke, Robert, Ritter, Helge
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: Frontiers Media S.A. 2021
Aiheet:
Linkit: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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