A Method for Derivation of Robot Task-Frame Control Authority from Repeated Sensory Observations

In this letter, we propose a novel method that enables the robot to autonomously devise an appropriate control strategy from human demonstrations without a prior knowledge of the demonstrated task. The method is primarily based on observing the patterns and consistency in the observed dataset. This is obtained through a demonstration setting that uses a motion capture system, a force sensor, and muscle activity measurements. The variables (position and force) in the collected dataset are then segmented and analysed for each axis of the observed task frame separately. While checking several conditions based on the consistency, value range, and magnitude of repeated observations, the appropriate controller (i.e., position or force) is delegated to each axis of the task frame. In the final stage, the method also checks for a correlation between variables and muscle activity patterns to determine the desired stiffness behaviour. The robot then uses the derived control strategies in autonomous operation through a hybrid force/impedance controller. To validate the proposed method, we performed experiments on real-life tasks involving physical interaction with the environment, where we considered surface wiping, material sawing, and drilling.