Hierarchical Reinforcement Learning for Quadrupedal Robots: Efficient Object Manipulation in Constrained Environments
This study introduces a hierarchical reinforcement learning (RL) framework tailored to object manipulation tasks by quadrupedal robots, emphasizing their real-world deployment. The proposed approach adopts a sensor-driven control structure capable of addressing challenges in dense and cluttered envi...
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| Main Authors: | David Azimi, Reza Hoseinnezhad |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/5/1565 |
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