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Robotic Arm Drift Compensation Using Adaptive Computed Torque Control Based on Reinforcement Learning

Year: 2025

Type: article

Abstract: Robotic arms often experience drift over time due to various factors such as temperature fluctuations, gearbox backlash, wear and tear, sensor inaccuracies, and changes in load. While model-based methods have addressed drift compensation, they come with considerable limitations compared to data-driven approaches. This paper will review the PID, Computed Torque Control and Adaptive Computed Torque Control based on Reinforcement Learning, soft actor critic (SAC) model, examining their strengths, weaknesses, and potential areas for improvement to enhance the accuracy and precision of robotic systems in industrial settings.

Source: International Journal For Multidisciplinary Research

DOI: 10.36948/ijfmr.2025.v07i03.47315

Authors: Pride Mashiyani, Didymus Tanyaradzwa Makusha -

Institution: Harare Institute of Technology

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4411215488

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