Vaehrens, Lars , ALVAREZ LORENZO, DANIEL, Berger, Ulrich , Bogh, Simon , Wani, MA , Kantardzic, M , Palade, V , Neagu, D , Yang, L , Chan, KY
No
2022 21st Ieee International Conference On Machine Learning And Applications, Icmla
Proceedings Paper
Científica
01/01/2022
000980994900191
We present a deep reinforcement learning-based approach to control robotic manipulators and construct task-independent trajectories for point-to-point motions. The research objective in this work is to learn control in the joint action space, which can be generalized to various industrial manipulators. The approach necessitates that the neural network learns a mapping from joint movements to the reward landscape determined by the distance to the goal and nearby obstacles. In addition, curriculum learning is embedded in this approach to facilitate learning by reducing the complexity of the environment. Conducted experiments demonstrate how the reinforcement learningbased approach can be applied to three different industrial manipulators in simulation with minimal configuration changes. The results of our contribution demonstrate that a model can be trained in a simulation environment, transferred to the real world, and used in complex environments. Furthermore, the Sim2Real transfer, augmented by curriculum learning, highlights that the robots behave in the same way in the real world as in the simulation and that the operations in the real world are safe from a control and trajectory point-of-view.
Deep Reinforcement Learning; Industrial Manipulators; Path Planning