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track of the user

track of the robot

Figure 5: Taking the robot to the Figure 6: Tracking the user. The white area is the detected free
destination. space.
elevator door wall

robot position

Figure 7: Teaching the elevator Figure 8: Elevator detection from Figure 9: A detected but-
position to the robot. the LRF data. ton outside the elevator.
Teaching the Button Position The robot then asks where the buttons are, and the user indicates their
rough position. The robot searches the indicated area on the wall for image patterns which match the
given button models (e.g., circular or rectangular). Fig. 9 shows an example of detected button. The
position of the button with respect to the elevator coordinates and the button view, which is used as an
image template, are recorded after the verification by the user. The robot learns the buttons inside the
elevator in a similar way; the user indicates the position of the button box, and the robot searches there
for buttons.

CONCLUSION
This paper has described a method of interactively teaching the task of taking elevators to a mobile
robot. The method uses task models for describing the necessary pieces of knowledge for each task and
their dependencies. Task models include the following three kinds of robot-specific knowledge: object
models, motion models, and sensing skills. Using the task model, the robot can determine what pieces
of knowledge are further needed, and plans necessary interactions with users to obtaining them. By this
method, the user can teach only the important pieces of task knowledge easily and efficiently. We have
shown the preliminary implementation and experimental results on the take-an-elevator task.
Currently the task model is manually designed for the specific, take-an-elevator task from scratch.
It would be desirable, however, that a part of existing task models can be reused for describing another.
Since reusable parts are in general commonly-used, typical operations, a future work is to develop a
repertoire of typical operations by, for example, using an inductive learning-based approach (Dufay
and Latombe 1984, Tsuda, Ogata, and Nanjo 1998). By using the repertoire, the user's effort for task
modeling is expected to be reduced drastically.
Another issue is the development of teaching procedures. Although the mechanism of determining
missing pieces of knowledge in a dependency network is general, for each missing piece, the corre-
sponding procedure for obtaining it from the user should be provided. Such teaching procedures are
also designed manually at present and, therefore, the kinds of pieces of knowledge that can be taught