  Tidy-up tasks by service robots in home environments are challenging in
robotics applications because they involve various interactions with the
environment. In particular, robots are required not only to grasp, move, and
release various home objects but also to plan the order and positions for
placing the objects. In this paper, we propose a novel planning method that can
efficiently estimate the order and positions of the objects to be tidied up by
learning the parameters of a probabilistic generative model. The model allows a
robot to learn the distributions of the co-occurrence probability of the
objects and places to tidy up using the multimodal sensor information collected
in a tidied environment. Additionally, we develop an autonomous robotic system
to perform the tidy-up operation. We evaluate the effectiveness of the proposed
method by an experimental simulation that reproduces the conditions of the Tidy
Up Here task of the World Robot Summit 2018 international robotics competition.
The simulation results show that the proposed method enables the robot to
successively tidy up several objects and achieves the best task score among the
considered baseline tidy-up methods.
