  Intelligent robots must be able to perform safe and efficient motion planning
in their environments. Central to modern motion planning is the configuration
space. Configuration spaces define the set of configurations of a robot that
result in collisions with obstacles in the workspace, C-clsn, and the set of
configurations that do not, C-free. Modern approaches to motion planning first
compute the configuration space and then perform motion planning using the
calculated configuration space. Real-time motion planning requires accurate and
efficient construction of configuration spaces.
  We are the first to apply a convolutional encoder-decoder framework for
calculating highly accurate approximations to configuration spaces. Our model
achieves an average 97.5% F1-score for predicting C-free and C-clsn for 2-D
robotic workspaces with a dual-arm robot. Our method limits undetected
collisions to less than 2.5% on robotic workspaces that involve translation,
rotation, and removal of obstacles. Our model learns highly transferable
features between robotic workspaces, requiring little to no fine-tuning to
adapt to new transformations of obstacles in the workspace.
