Search for Articles: for Articles : Sensors All Article Types Search Select an Option Select an Option Logical Operator Operator ► ▼ Biswanath Samanta /ajax/scifeed/subscribe Article Views Citations - Altmetric Altmetric Share Help Cite Discuss in SciProfiles Arial Georgia Verdana Aa Aa Aa       Open Access Article David AzimiDavid Azimi SciProfiles Scilit Preprints.org Google Scholar 1 and David Azimi Reza HoseinnezhadReza Hoseinnezhad SciProfiles Scilit Preprints.org Google Scholar 2,* Reza Hoseinnezhad Submission received: 10 January 2025 Revised: 3 February 2025 Accepted: 17 February 2025 Published: 4 March 2025 m n t J n t t t low-level T J Int. J. Robot. Res. 43 Sci. Robot. 9 Int. J. Robot. Res. 39 arXiv arXiv arXiv arXiv arXiv arXiv IEEE Robot. Autom. Lett. 9 IEEE Trans. Autom. Control. 68 IEEE Trans. Control. Netw. Syst. 10 arXiv Intell. Robot. 2 IEEE Robot. Autom. Lett. 8 IEEE Trans. Geosci. Remote. Sens. 62 | | | | | | | Sensors, EISSN 1424-8220, Published by MDPI RSS Content Alert Back to Top Top You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader. All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess. Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers. Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal. Original Submission Date Received: . Find support for a specific problem in the support section of our website. Please let us know what you think of our products and services. Visit our dedicated information section to learn more about MDPI. Azimi, D.; Hoseinnezhad, R. Hierarchical Reinforcement Learning for Quadrupedal Robots: Efficient Object Manipulation in Constrained Environments. Sensors 2025, 25, 1565. https://doi.org/10.3390/s25051565 Azimi D, Hoseinnezhad R. Hierarchical Reinforcement Learning for Quadrupedal Robots: Efficient Object Manipulation in Constrained Environments. Sensors. 2025; 25(5):1565. https://doi.org/10.3390/s25051565 Azimi, David, and Reza Hoseinnezhad. 2025. "Hierarchical Reinforcement Learning for Quadrupedal Robots: Efficient Object Manipulation in Constrained Environments" Sensors 25, no. 5: 1565. https://doi.org/10.3390/s25051565 Azimi, D., & Hoseinnezhad, R. (2025). Hierarchical Reinforcement Learning for Quadrupedal Robots: Efficient Object Manipulation in Constrained Environments. Sensors, 25(5), 1565. https://doi.org/10.3390/s25051565 ZIP-Document (ZIP, 8016 KiB) Azimi, D.; Hoseinnezhad, R. Hierarchical Reinforcement Learning for Quadrupedal Robots: Efficient Object Manipulation in Constrained Environments. Sensors 2025, 25, 1565. https://doi.org/10.3390/s25051565 Azimi D, Hoseinnezhad R. Hierarchical Reinforcement Learning for Quadrupedal Robots: Efficient Object Manipulation in Constrained Environments. Sensors. 2025; 25(5):1565. https://doi.org/10.3390/s25051565 Azimi, David, and Reza Hoseinnezhad. 2025. "Hierarchical Reinforcement Learning for Quadrupedal Robots: Efficient Object Manipulation in Constrained Environments" Sensors 25, no. 5: 1565. https://doi.org/10.3390/s25051565 Azimi, D., & Hoseinnezhad, R. (2025). Hierarchical Reinforcement Learning for Quadrupedal Robots: Efficient Object Manipulation in Constrained Environments. Sensors, 25(5), 1565. https://doi.org/10.3390/s25051565 Subscribe to receive issue release notifications and newsletters from MDPI journals