[4L2] [General Session] 11. Robot / Real World

[4L2-01] Learning and Generation of Actions from Teleoperation for Domestic Service Robots

〇Kensuke Iwata¹, Tatuya Aoki¹, Takato Horii¹, Tomoaki Nakamura¹, Takayuki Nagai¹ (1. The University of Electro-Communications.)

Keywords:AI, Domestic Service Robot, Motion Learning

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