![](bg4.png)
this particular case, the system can generate the stand
up motion with only GP
5
. Figure 9 shows the snap-
shots of a standing motion by our system, where the
gradient of ground is 10.0 (deg) backward. In this
particular case, the system can generate the stand up
motion with only GP
1
and GP
3·4
. Figure 10 shows
the trajectory of the gradient of HOAP-1 body when
it executes the motions obtained by search. In the fig-
ure, solid, dashed and dotted lines show the trajectory
of the gradient of HOAP-1’s body standing up from
chair on the flat, the forward slope and the backward
slope, respectively. The two broken lines, at the be-
ginning of the motion, show that the gradients of the
body on the slopes are both different from that on the
flat. The difference corresponds to the gradient of the
slope. This is obvious, for HOAP-1 is sitting on a
chair on the slope. Through the movement, the differ-
ence of the gradient is attenuated gradually. The re-
sults indicates that the motion control adapts the mo-
tion to the different environments.
5 CONCLUSION
This paper proposed a concept learning-based ap-
proach to motion control for humanoid robots. The
motion generation system had been implemented with
decision tree learner C4.5 and depth first search tech-
nique. Some stable and anti-tumble motions to stand
up from a chair were performed by humanoid robot
HOAP-1. In future work, we will dedicate to the im-
provement of our system for more complex motions
and to the investigation of the relations of the suitable
number between guideposts and examples for learn-
ing.
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guidepost ID 0 1 1 1
time (msec) 0 1000 2000 3000
guidepost ID 5 5 5 FINAL
time (msec) 3500 4250 5000 6250
Figure 9: The snapshot of a standing motion generated by
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Figure 10: Trajectory of gradient of the body changing gra-
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