  We propose a new method for collision-free planning using Conditional
Generative Adversarial Networks (cGANs) to transform between the robot's joint
space and a latent space that captures only collision-free areas of the joint
space, conditioned by an obstacle map. Generating multiple plausible
trajectories is convenient in applications such as the manipulation of a robot
arm by enabling the selection of trajectories that avoids collision with the
robot or surrounding environment. In the proposed method, various trajectories
that avoid obstacles can be generated by connecting the start and goal state
with arbitrary line segments in this generated latent space. Our method
provides this collision-free latent space, after which any planner, using any
optimization conditions, can be used to generate the most suitable paths on the
fly. We successfully verified this method with a simulated and actual UR5e
6-DoF robotic arm. We confirmed that different trajectories could be generated
depending on optimization conditions.
