Learning Multiple Sensorimotor Units to Complete Compound Tasks using an RNN with Multiple Attractors As the complexity of the robotxe2x80x99s 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 visuomotorcontrol 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 pseudstatemachine model with a constraint in hidden values. We implemented this framework on two different robots to allow their performance of repetitive tasks with errorrecovery 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.

Learning to Sequence Multiple Tasks with Competing Constraints Imitation learning offers a general framework where robots can efficiently acquire novel motor skills from demonstrations of a human teacher. While many promising achievements have been shown, the majority of them are only focused on singlestroke movements, without taking into account the problem of multitasks sequencing. Conceivably, sequencing different atomic tasks can further augment the robotxe2x80x99s capabilities as well as avoid repetitive demonstrations. In this paper, we propose to address the issue of multitasks sequencing with emphasis on handling the socalled competing constraints, which emerge due to the existence of the concurrent constraints from Cartesian and joint trajectories. Specifically, we explore the null space of the robot from an informationtheoretic perspective in order to maintain imitation fidelity during transition between consecutive tasks. The effectiveness of the proposed method is validated through simulated and real experiments on the iCub humanoid robot.