In this research, we examined the occurrence of skill mutation when propagating a collaborative sawing task from robot-to-robot. We conducted this research, to gain insight into this mutation to understand the generation of potentially beneficial or dangerous skills.
Thirty propagation steps in simulation were conducted per experiment, each consisting of one expert robot teaching a novice (learner) robot. To explore what influences mutation, different external factors were changed, such as the maximum stiffness of the robots, the base position of the robots, the friction coefficient of the object and saw, and the period span of one sawing movement. The robots were controlled using a hybrid force/impedance controller, with the impedance part responsible for the sawing movement. The goal of the skill propagation was to teach the novice robot the impedance controller inputs (desired trajectory and stiffness), which is implemented through a three-staged learning process. In stage one, the desired trajectory was learned by encoding the measured trajectory using DMP and LWR, in stage two the stiffness was learned by encoding the computed stiffness, and in stage three the novice robot became an expert, able to collaboratively execute the task.
The results showed that the skill varied over the different propagation step, therefore proven the existence of skill mutation. It was found that the biggest mutations were caused by a phase lag, overshoot on the desired trajectory, differences in joint states, and reaching torque limits. It was also found that environmental boundaries limited the mutations. By comparing the results of the different propagation steps of both different and the same conditions, it was that the mutations were not reproducible. This is a result of not being able to fix all external factors. We also identified the benefits (skill useful for different settings or different tasks, and energy efficiency) and dangers/drawbacks (high forces and skill becoming useless for initial task) of the mutation.