A General Approach for the Automation of Hydraulic Excavator Arms Using Reinforcement Learning

RA-L/ICRA 2021 Abstract: This article presents a general approach to derive an end effector trajectory tracking controller for highly nonlinear hydraulic excavator arms. Rather than requiring an analytical model of the system, we use a neural network model that is trained based on measurements collected during operation of the machine. The data-driven model effectively represents the actuator dynamics including the cylinder-to-joint-space conversion. Requiring only the distances between the individual join