Learning Mobile Manipulation through Deep Reinforcement Learning
Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique...
Main Authors: | Cong Wang, Qifeng Zhang, Qiyan Tian, Shuo Li, Xiaohui Wang, David Lane, Yvan Petillot, Sen Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-02-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/3/939 |
Similar Items
-
Pick and Place Operations in Logistics Using a Mobile Manipulator Controlled with Deep Reinforcement Learning
by: Ander Iriondo, et al.
Published: (2019-01-01) -
Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review
by: Rongrong Liu, et al.
Published: (2021-01-01) -
Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence
by: Recep Ozalp, et al.
Published: (2024-01-01) -
A Deep Reinforcement Learning Strategy Combining Expert Experience Guidance for a Fruit-Picking Manipulator
by: Yuqi Liu, et al.
Published: (2022-01-01) -
Space Manipulator Collision Avoidance Using a Deep Reinforcement Learning Control
by: James Blaise, et al.
Published: (2023-08-01)