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Hierarchical Reinforcement Learning for Quadrupedal Robots: Efficient Object Manipulation in Constrained

David Azimi¹, Reza Hoseinnezhad²

¹School of Information Technology, Deakin University, Victoria 3125, Australia.

Sensors (Basel, Switzerland)

|March 17, 2025

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This study presents a hierarchical reinforcement learning (RL) framework for quadrupedal robots performing object manipulation. The sensor-driven approach enhances real-world deployment in cluttered spaces with high accuracy and efficiency.

Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Quadrupedal robots require advanced manipulation capabilities for real-world tasks.
Operating in cluttered environments presents significant challenges for robot navigation and manipulation.
Existing control structures often face limitations in adaptability and computational efficiency.

Purpose of the Study:

To introduce a novel hierarchical reinforcement learning (RL) framework for object manipulation by quadrupedal robots.
To develop a sensor-driven control structure for robust operation in dense, cluttered environments.
To optimize decision-making using a novel, sensor-based reward function for enhanced adaptability and efficiency.

Main Methods:

Implementation of a hierarchical reinforcement learning (RL) framework.

Keywords:

quadrupedal robots reinforcement learning robotic manipulation

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Development of a sensor-driven control architecture incorporating obstacle avoidance.

Design of a novel reward function utilizing sensor-based obstacle data.

Simulation experiments conducted in NVIDIA Isaac Sim with ANYbotics quadrupedal robots.

Main Results:

Achieved high object manipulation accuracy with a mean positioning error of 11 cm.
Demonstrated successful operation across object-target distances up to 10 meters.
Validated effective integration of path planning in complex, obstacle-filled environments.
Showcased energy-efficient and stable robotic operations.

Conclusions:

The proposed RL framework offers a versatile and efficient solution for quadrupedal robot object manipulation.
The sensor-driven approach enhances robustness and adaptability in real-world, cluttered scenarios.
This framework represents a significant advancement for practical robotic applications demanding precision and efficiency.