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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Bibliographic Details
Main Authors: Andrew Melnik, Luca Lach, Matthias Plappert, Timo Korthals, Robert Haschke, Helge Ritter
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-06-01
Series:Frontiers in Robotics and AI
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/frobt.2021.538773/full