| dc.description.abstract | This Master?s thesis implements a multiple paired models architecture that is
used to control a simulated robot. The architecture consists of several modules.
Each module holds a paired forward/inverse model. The inverse model takes as
input the current and desired state of the system, and outputs motor commands
that will achieve the desired state. The forward model takes as input the current
state and the motor commands acting on the environment, and outputs the
predicted next state. The models are paired, due to the fact that the output
of the inverse model is fed into the forward model. A weighting mechanism
based on how well the forward model predicts determines how much a module
will influence the total motor control. The architecture is a slight tweak of the
HAMMER and MOSAIC architectures of Demiris and Wolpert, respectively.
The robot is to imitate dance moves that it sees. Three experiments are
done; in the first two the robot imitates another robot, whereas in the third
experiment the robot imitates a movement pattern gathered from human data.
The pattern was obtained using a Pro Reflex tracking system. After training
the multiple paired models architecture, the performance and self-organization
of the different modules are analyzed. Shortcomings with the architecture are
pointed out along with directions for future work.
The main results of this thesis is that the architecture does not self-organize
as intended; instead the architecture finds its own way to separate the input
space into different modules. This is also most likely attributed to a problem
with the learning of the responsibility predictor of the modules. This problem
must be solved for the architecture to work as designed, and is a good starting
point for future work. | |
