Task Space Control of Hydraulic Construction Machines using Reinforcement Learning

2023/07/18 by Hyung Joo Lee, Lee, Hyung Joo, Sigrid Brell‐Çokcan +1
Engineering · #FOS: Computer and information sciences #Hydraulic and Pneumatic Systems #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2307.09246

openalex publication_date 2023/07/18 · openalex created_date 2023/07/20 · openalex updated_date 2026/07/28

Abstract

Teleoperation is vital in the construction industry, allowing safe machine manipulation from a distance. However, controlling machines at a joint level requires extensive training due to their complex degrees of freedom. Task space control offers intuitive maneuvering, but precise control often requires dynamic models, posing challenges for hydraulic machines. To address this, we use a data-driven actuator model to capture machine dynamics in real-world operations. By integrating this model into simulation and reinforcement learning, an optimal control policy for task space control is obtained. Experiments with Brokk 170 validate the framework, comparing it to a well-known Jacobian-based approach.

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