  The environments of such large industrial machines as waste cranes in waste
incineration plants are often weakly observable, where little information about
the environmental state is contained in the observations due to technical
difficulty or maintenance cost (e.g., no sensors for observing the state of the
garbage to be handled). Based on the findings that skilled operators in such
environments choose predetermined control strategies (e.g., grasping and
scattering) and their durations based on sensor values, %thereby improving the
robustness of their actions, we propose a novel non-parametric policy search
algorithm: Gaussian process self-triggered policy search (GPSTPS). GPSTPS has
two types of control policies: action and duration. A gating mechanism either
maintains the action selected by the action policy for the duration specified
by the duration policy or updates the action and duration by passing new
observations to the policy; therefore, it is categorized as self-triggered.
GPSTPS simultaneously learns both policies by trial and error based on sparse
GP priors and variational learning to maximize the return. To verify the
performance of our proposed method, we conducted experiments on
garbage-grasping-scattering task for a waste crane with weak observations using
a simulation and a robotic waste crane system. As experimental results, the
proposed method acquired suitable policies to determine the action and duration
based on the garbage's characteristics.
