Abstract: A dipper for a work machine is disclosed. The dipper may have a front cavity wall forming an inlet which defines a vertical inlet plane, a rear cavity wall opposite the front cavity wall forming an outlet which defines an outlet plane, a top cavity wall extending between front and rear cavity walls defining a horizontal top plane, a bottom cavity wall opposite the top cavity wall defining a bottom plane, and two side cavity walls. The two side cavity walls may further extend between the front and rear cavity wall. The outlet plane may be angled relative to the inlet plane, the bottom plane may be angled relative to the top plane, and the outlet plane is perpendicular to the bottom plane.

Abstract: A method of using a virtual machine operator model includes providing a virtual machine operating environment having a current state and including a virtual machine and a virtual operator agent acting within the virtual machine operating environment. The method also includes executing a control action, by the virtual operator agent, relative to the virtual machine based on the virtual machine operator model. The method also includes analyzing a new state of the virtual machine operating environment resulting from execution of the control action, assigning a positive reward or a negative reward to the control action based on the new state, assigning the negative reward to the control action resulting in a high implement jerk movement of the virtual machine, and executing a learning algorithm to update the virtual machine operator model based on the positive reward or the negative reward.

Abstract: A method of using a virtual machine operator model includes providing a virtual machine operating environment having a current state and including a virtual machine and a virtual operator agent acting within the virtual machine operating environment. The method also includes executing a control action, by the virtual operator agent, relative to the virtual machine based on the virtual machine operator model. The method also includes analyzing a new state of the virtual machine operating environment resulting from execution of the control action, assigning a positive reward or a negative reward to the control action based on the new state, assigning the negative reward to the control action resulting in a high implement jerk movement of the virtual machine, and executing a learning algorithm to update the virtual machine operator model based on the positive reward or the negative reward.