Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence

Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence

This article presents a literature review of the past five years of studies using Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic manipulation tasks. The reviewed articles are examined in various categories, including DRL and IRL for perception, assembly, manipu...

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Hlavní autoři: Recep Ozalp, Aysegul Ucar, Cuneyt Guzelis
Médium: Článek
Jazyk:English
Vydáno: IEEE 2024-01-01
Edice:IEEE Access
Témata:
Deep reinforcement learning
inverse reinforcement learning
robotic manipulation
artificial intelligence
trustworthy AI
interpretable AI
On-line přístup:https://ieeexplore.ieee.org/document/10493015/

Internet

https://ieeexplore.ieee.org/document/10493015/

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