A motion imitation system for humanoid robots with inference-based optimization and an auditory user interface

Humanoid robots are expected to perform various jobs replacing human beings, but it is difficult for non-expert users to control them. To control humanoid robots easily, various motion imitation systems, in which a humanoid robot does the same motion as a human teacher, have been studied. In the present study, we make two contributions to building easy-to-use imitation systems. The first contribution is a flexible learning system that enables both synchronous real-time imitation, in which the robot imitates human motion almost immediately, and batch imitation, in which the robot’s motion is optimized more accurately after longer measurement of human motion. We use an efficient inference-based optimization method to enable both learning modes. The other contribution is an auditory user interface that accepts voice commands from the user and at the same time identifies the target person to be imitated. This enables the user to easily use the imitation system even when other people are present. Experimental results show that the proposed system can be used in both imitation modes and that it can identify the target person.