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2.4 Explaining and Generalizing
Modules
Explaining and generalizing modules constitute the
EBL process of the apprentice agent. Our past work
(Wang, L., Tian, Y. and Sawaragi, T., 2008) has
described its detailed working mechanism.
The EBL process both can learn from examples
directly given by human workers and can learn from
examples generated by the adapting module and
further revised by human workers. The learning
results include: 1. a retrieving rule for the case, 2. the
case, and 3. adapting rules of the case. The adapting
rules are learned by comparing the revised solution
with the adapted solution.
3 AN EXAMPLE
Figure 2 shows an application example of the
apprentice agent. In this example, the apprentice
agent assists human workers by generating robot
programs for palletizing two rows of blocks into a
plate. The blocks in the same row have the same
cross-section but different heights. The blocks in the
left row have bigger widths (i.e., are thicker) than
those in the right row.
Figure 2: An example: palletizing blocks.
First, human workers teach the robot how to
palletize the blue block in the right row. Then the
apprentice agent reuses this case successfully in
palletizing the rest blocks in the left row.
However, when the case is reused in palletizing
the first (i.e., the blue) block in the left row, an error
occurs. The robot tool collides with the target blue
block. Then human workers revise the adapted
solution by inserting a command to slow down the
robot speed before the command of closing the robot
tool. The revised solution can be executed without
errors. The EBL process learns a new case from the
revised solution and an adapting rule that if the
width of the workpiece is not much smaller than (i.e.,
>80% of) the open width of the robot tool, then robot
should slow down before gripping the workpiece.
The apprentice agent reuses the new learned case in
palletizing the rest blocks of the left row successfully.
4 CONCLUSIONS
We propose a method that integrates CBR and EBL
in an apprentice agent to solve the utility problem of
CBR. Its distinctive feature is applying EBL in post-
processing an observed case to reduce its reusing and
adapting cost. While this apprentice agent can be
used for general purposes, in this paper we apply it
in the robotic assembly domain.
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