Learning and Generation of Actions from Teleoperation for Domestic Service Robots

Abstract

In this paper, we propose a method for motion learning aimed at the execution of autonomous household chores by service robots in real environments. For robots to act autonomously in a real environment, it is necessary to define the appropriate actions for the environment. However, it is difficult to define these actions manually. Therefore, body motions that are common to multiple actions are defined as motion primitives. Complex actions can then be learned by combining these motion primitives. For learning motion primitives, we propose a reference-point and object-dependent Gaussian process hidden semi-Markov model (RPOD-GP-HSMM). For verification, a robot is teleoperated to perform the actions included in several domestic household chores. The robot then learns the associated motion primitives from the robot's body information and object information.