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TEACHING A MOBILE ROBOT TO TAKE ELEVATORS

Koji Iwase, Jun Miura, and Yoshiaki Shirai
Department of Mechanical Engineering, Osaka University
Suita, Osaka 565-0871, Japan
ABSTRACT
The ability of moving between floors by using elevators is indispensable for mobile robots operating
in office environments to expand their work areas. This paper describes a method of interactively teach-
ing the task of taking elevators for making it easier for the user to use such robots for various elevators.
The necessary knowledge of the task is organized as the task model. The robot examines the task model
and determines what are missing in the model, and then asks the user to teach them. This enables the
user to teach the necessary knowledge easily and efficiently. Experimental results show the potential
usefulness of our approach.

KEYWORDS
Mobile robots, Interactive teaching, Task models, Take an elevator, Visual navigation.
INTRODUCTION
The ability of moving between floors by using elevators is indispensable for mobile robots perform-
ing service tasks in office environments to extend their working areas. We have developed a mobile
robot that can take elevators, but we had to give the robot in advance the necessary knowledge such as
the shape of the elevator and the positions of the buttons. Since the necessary knowledge of the task
of taking elevators is different from place to place, it is desirable that the user can easily teach such
knowledge on-site.
We have been developing a teaching framework called task model-based interactive teaching (Miura
et al. 2004), in which the robot examines the description of a task, called task model, to determine miss-
ing pieces of necessary knowledge, and actively asks the user to teach them. We apply this framework
to the task of taking elevators (take-an-elevator task) by our robot (see Fig. 1). This paper describes the
task models and the interactive teaching method with several teaching examples.
TASK MODEL-BASED INTERACTIVE TEACHING
Interaction between the user and a robot is useful for an efficient and easy teaching of task knowledge.
Without interaction, the user has to think by himself/herself about what to teach to the robot. This is
difficult for the user partly because he/she does not have enough knowledge of the robot's ability (i.e.,
what the robot can (or cannot) do), and partly because the user's knowledge may not be well-structured.
If the robot knows of what are needed for achieving the task, then the robot can ask the user to teach
them; this enables the user to easily give necessary knowledge to the robot. This section explains the
representations for task models and the teaching strategy.