The aim of this
proposal is to implement and improve different bioinspired balance strategies
and controllers while the humanoid robot performs a special manipulation task.
The behavior proposed consist of transport an object without grasping it,
similar to a human waiter transporting objects on a tray. During the execution
of this behavior a double balancing task must be performed. The robot must keep
its own equilibrium meanwhile keeping the object on the tray, that is,
preserving its equilibrium and its condition.
The testbeds it
will be possible to evaluate robot performance in a wide variety of situations
that could occur in the real world. This constitutes an advantage in the development
of balance controllers using bioinspired techniques, such as Neural Networks,
Fuzzy systems, etc. The data from experiments is applied in a behavior learning
process. The richer is the information used in this process, the better the
controller performance will be. The experiments will be carried out with the
humanoid robot REEM-C and the results will also be compared with the data
obtained with the humanoid robot TEO from the RoboticsLab research group. This
will enrich the final outcomes.