Learning Multiple Sensorimotor Units to Complete Compound Tasks using an RNN with Multiple Attractors

As the complexity of the robot's tasks increases, we can consider many general tasks in a compound form that consists of shorter tasks. Therefore, for robots to generate various tasks, they need to be able to execute shorter tasks in succession, appropriately to the situation. With the design principle to construct the architecture for robots to execute complex tasks compounded with multiple subtasks, this study proposes a visuomotor-control framework with the characteristics of a state machine to train shorter tasks as sensorimotor units. The design procedure of training framework consists of 4 steps: (1) segment entire task into appropriate subtasks, (2) define subtasks as states and transitions in a state machine, (3) collect subtasks data, and (4) train neural networks: (a) autoencoder to extract visual features, (b) a single recurrent neural network to generate subtasks to realize a pseud-state-machine model with a constraint in hidden values. We implemented this framework on two different robots to allow their performance of repetitive tasks with error-recovery motion, subsequently, confirming the ability of the robot to switch the sensorimotor units from visual input at the attractors of the hidden values created by the constraint.