Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning
Authors
Keywords
-
Journal
AUTONOMOUS ROBOTS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-02-10
DOI
10.1007/s10514-022-10034-z
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning
- (2021) Asad Ali Shahid et al. Applied Sciences-Basel
- Robot control parameters auto-tuning in trajectory tracking applications
- (2020) Loris Roveda et al. CONTROL ENGINEERING PRACTICE
- Grasping in the Wild: Learning 6DoF Closed-Loop Grasping From Low-Cost Demonstrations
- (2020) Shuran Song et al. IEEE Robotics and Automation Letters
- Optimal Elevator Group Control via Deep Asynchronous Actor–Critic Learning
- (2020) Qinglai Wei et al. IEEE Transactions on Neural Networks and Learning Systems
- A Survey on Learning-Based Approaches for Modeling and Classification of Human–Machine Dialog Systems
- (2020) Fuwei Cui et al. IEEE Transactions on Neural Networks and Learning Systems
- Hierarchical Deep Reinforcement Learning for Continuous Action Control
- (2018) Zhaoyang Yang et al. IEEE Transactions on Neural Networks and Learning Systems
- Grasp Pose Detection in Point Clouds
- (2017) Andreas ten Pas et al. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
- Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
- (2017) Sergey Levine et al. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
- Human–robot interaction review and challenges on task planning and programming
- (2016) Panagiota Tsarouchi et al. INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
- Mastering the game of Go with deep neural networks and tree search
- (2016) David Silver et al. NATURE
- Autonomous Driving in the iCity—HD Maps as a Key Challenge of the Automotive Industry
- (2016) Heiko G. Seif et al. Engineering
- Deep learning for detecting robotic grasps
- (2015) Ian Lenz et al. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
- Human-level control through deep reinforcement learning
- (2015) Volodymyr Mnih et al. NATURE
- Industry 4.0
- (2014) Heiner Lasi et al. Business & Information Systems Engineering
- Learning to select and generalize striking movements in robot table tennis
- (2013) Katharina Mülling et al. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
- Natural Actor-Critic
- (2008) Jan Peters et al. NEUROCOMPUTING
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started