Abstract: This study presents a deep reinforcement learning (DRL) approach to train a robotic arm for object reaching tasks in industrial settings, eliminating the need for traditional task-specific programming. Leveraging the Proximal Policy Optimization (PPO) algorithm for its stability in continuous control, the system learns optimal behaviors through autonomous trial-and-error. Central to this work is reward shaping, where structured feedback based on distance to the target, collision avoidance, motion constraints, and step efficiency guides the agent, akin to incremental coaching. A simulated industrial environment was developed using Webots, integrated with OpenAI Gym and Stable-Baselines3, enabling safe training with sensor data (camera, distance sensor) and randomized target placements. Three models with varying reward schemes were evaluated: simpler rewards prioritized rapid convergence, while complex formulations (e.g., perceptual alignment) enhanced long-term accuracy at the cost of initial instability. Experimental results demonstrated that reward shaping reduced the required steps, highlighting its role in accelerating learning. The study underscores the efficacy of combining DRL, simulation-based training, and adaptive reward design to develop efficient robotic controllers. These findings advance scalable solutions for industrial automation, emphasizing the trade-offs between reward complexity and policy convergence. Future work will refine reward functions to bridge simulation-to-reality gaps, fostering practical adoption in manufacturing and assembly systems.

John Mark Correa, Rudolph Joshua Candare and Junrie B. Matias, “Deep Reinforcement Learning Based Robotic Arm Control Simulation to Execute Object Reaching Task for Industrial Application” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160666

@article{Correa2025,
title = {Deep Reinforcement Learning Based Robotic Arm Control Simulation to Execute Object Reaching Task for Industrial Application},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160666},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160666},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {John Mark Correa and Rudolph Joshua Candare and Junrie B. Matias}
}