  In high dimensional robotic system, the manifold of the valid configuration
space often has a complex shape, especially under constraints such as
end-effector orientation or static stability. We propose a generative
adversarial network approach to learn the distribution of valid robot
configurations under such constraints. It can generate configurations that are
close to the constraint manifold. We present two applications of this method.
First, by learning the conditional distribution with respect to the desired
end-effector position, we can do fast inverse kinematics even for very high
degrees of freedom (DoF) systems. Then, we use it to generate samples in
sampling-based constrained motion planning algorithms to reduce the necessary
projection steps, speeding up the computation. We validate the approach in
simulation using the 7-DoF Panda manipulator and the 28-DoF humanoid robot
Talos.

Robotics;Machine Learning