Continuous Motion Planning for Industrial Robots based on Direct Sensory Input

Abstract

Motion planning processes for industrial robots are complex tasks often done manually by human domain experts and result in robotic motion that lacks flexibility and adaptability in response to dynamic environments. In our work, we propose an automated control agent utilizing a convolutional neural network embedded in an actor-critic architecture that learns adaptive continuous motion behavior via reinforcement learning. The learning is based on direct sensory input without the need of directly programming the robot’s motion. We show that the learned behavior can account for uncontrollable dynamic environmental circumstances and helps to decrease time and cost of ramp up processes.