Acquisition of Task Performance Skills from a Human Expert for Teaching a Machining Robot

A new method for acquiring task performance skills from human experts is presented. In grinding and deburring, a human expert moves a tool at an optimal feedrate and cutting force as well as with an appropriate compliance for holding the tool. An experienced worker can select the correct strategy for performing a task and change it dynamically in accordance with the task process state. In this paper, the human expertise for selecting a task strategy that accords with the process characteristics is modeled as an associative mapping, and represented and generated by using a neural network. First, the control strategy for manipulating a tool is described in terms of feedforward inputs and tool holding dynamics. The parameters and variables representing the control strategy are then identified by using teaching data taken from a demonstration by an expert. The task process is also modeled and characterized by a set of parameters, which are identified by using this same teaching data. Combining the two sets of identified parameters, we can derive an associative mapping from the task process characteristics to the task strategy parameters. Using a nueral network, this associative mapping is generated through learning operations. The method is applied to grinding, and implemented on a direct-drive robot. It is shown that the robot is able to associate a correct control strategy with process characteristics in a manner similar to that of the human expert.