Developing New Abilities for Humanoid Robots with a Wearable Interface

Abstract

We focused on a method to enhance the abilities of humanoid robots by paying attention to the fact that imitation is the best way to learn a new ability. We designed and developed a wearable interface which is lightweight and presents multi-modal communication channels for interacting with robots. And we proposed a method to build motion primitives from the user demonstrated motion using curve simplification and clustering. A stochastic process is used for modelling motions and generating new motions. The stochastic model presents the way of generating various motions not monotonous repetition of demonstrated motions. And we tested our method by using a humanoid robot AMIO. The limitations of our work are 1) limited working space of human user because our wearable interface uses magnetic sensors which can be operated near the origin sensor, and 2) the motions that we generated are not considering the meaning of the task. For the further work, we will replace the magnetic sensors with the other positioning sensors that do not have any spatial limitations. And for improving the intelligence of humanoid robots, defining task descriptors and extracting task descriptors from a demonstrated task are indispensable. We are planning to conduct a research on task description method for generating tasks with ease.