Reinforcement learning of a motor skill for wearing a T-shirt using topology coordinates

This article focuses on learning motor skills for anthropomorphic robots that must interact with non-rigid materials to perform such tasks as wearing clothes, turning socks inside out, and applying bandages. We propose a novel reinforcement learning framework for learning motor skills that interact with non-rigid materials. Our learning framework focuses on the topological relationship between the configuration of the robot and the non-rigid material based on the consideration that most details of the material (e.g. wrinkles) are not important for performing the motor tasks. This focus allows us to define the task performance and provide reward signals based on a low-dimensional variable, i.e. topology coordinates, in a real environment using reliable sensors. We constructed an experimental setting with an anthropomorphic dual-arm robot and a tailor-made T-shirt for it. To demonstrate the feasibility of our framework, a robot performed a T-shirt wearing task, whose goal was to put both of its arms into the corresponding sleeves of the T-shirt. The robot acquired sequential movements that put both of its arms into the T-shirt.