Bayesian Policy Optimization for Waste Crane with Garbage Inhomogeneity

Описание: Hikaru Sasaki, Terushi Hirabayashi, Kaoru Kawabata, Yukio Onuki, and Takamitsu Matsubara

IEEE RA-L with CASE'20 option

The objective of this study is to develop a framework
that can optimize control policies of a waste crane at a waste incineration
plant through an autonomous trial and error manner.
Since a waste crane is a massive mechanical system that moves
slowly and takes several minutes to execute a task, obtaining data
samples by executing tasks is very costly. Moreover, no sensors
are available that can observe the state of the grasped flammable
waste composed of various materials with different degrees of
hardness and wetness. Therefore, the inhomogeneity of waste
causes unpredictable fluctuation in the crane’s task performance.
To cope with these problems, we propose a framework for optimizing
the policy parameters of a parameterized control policy
with Multi-Task Robust Bayesian Optimization (MTRBO). Our framework features the following two characteristics: (1) outlier
robustness against garbage inhomogeneity and (2) sample reuse
from previously solved tasks to enhance its sample efficiency.
To investigate the effectiveness of our framework, we conducted
experiments on garbage-scattering tasks with (i) a robot waste
crane with pseudo-garbage and (ii) an actual waste crane at a
waste incineration plant. Experimental results demonstrate that
our framework robustly optimized the control policies of the
garbage cranes, even with a much-reduced amount of data under
the influence of garbage inhomogeneity.