  When robots work in a cluttered environment, the constraints for motions
change frequently and the required action can change even for the same task.
However, planning complex motions from direct calculation has the risk of
resulting in poor performance local optima. In addition, machine learning
approaches often require relearning for novel situations. In this paper, we
propose a method of searching appropriate motions by using conditional
Generative Adversarial Networks (cGANs), which can generate motions based on
the conditions by mimicking training datasets. By training cGANs with various
motions for a task, its latent space is fulfilled with the valid motions for
the task. The appropriate motions can be found efficiently by searching the
latent space of the trained cGANs instead of the motion space, while avoiding
poor local optima. We demonstrate that the proposed method successfully works
for an object-throwing task to given target positions in both numerical
simulation and real-robot experiments. The proposed method resulted in three
times higher accuracy with 2.5 times faster calculation time than searching the
action space directly.
