Expert-Trajectory-Based Features for Apprenticeship Learning via Inverse Reinforcement Learning for Robotic Manipulation

Expert-Trajectory-Based Features for Apprenticeship Learning via Inverse Reinforcement Learning for Robotic Manipulation

This paper explores the application of Inverse Reinforcement Learning (IRL) in robotics, focusing on inferring reward functions from expert demonstrations of robot arm manipulation tasks. By leveraging IRL, we aim to develop efficient and adaptable techniques for learning robust solutions to complex...

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Bibliographic Details
Main Authors: Francisco J. Naranjo-Campos, Juan G. Victores, Carlos Balaguer
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Applied Sciences
Subjects:
machine learning
robotic manipulation
deep reinforcement learning
inverse reinforcement learning
artificial intelligence
Online Access:https://www.mdpi.com/2076-3417/14/23/11131
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