CONTROL OF INDUSTRIAL MANIPULATORS THROUGH REINFORCEMENT LEARNING: A STUDY OF THE PANDA MANIPULATOR IN A SIMULATED ENVIRONMENT
Marcelo Albergaria Paulino Fernandes Ferreira, Taniel Silva Franklin, Oberdan Rocha Pinheiro · 2023
The wide variety of scenarios in industrial environments requires intelligent robotics capable of directly interacting with the environment for problem-solving. Through reinforcement learning, robots can quickly adapt to new situations and learn from direct interaction with the environment. This work proposes a simulation environment based on Robotics Toolbox for Python to solve a classic problem of the inverse kinematics of manipulators, ensuring that the robot reaches the desired position without colliding with the obstacles present in the scene. The potential of this reinforcement learning method is illustrated through simulation using the Franka-Emika Panda manipulator trained by the Deep Deterministic Policy Gradient algorithm.