  Autonomous robots require online trajectory planning capability to operate in
the real world. Efficient offline trajectory planning methods already exist,
but are computationally demanding, preventing their use online. In this paper,
we present a novel algorithm called Guided Trajectory Learning that learns a
function approximation of solutions computed through trajectory optimization
while ensuring accurate and reliable predictions. This function approximation
is then used online to generate trajectories. This algorithm is designed to be
easy to implement, and practical since it does not require massive computing
power. It is readily applicable to any robotics systems and effortless to set
up on real hardware since robust control strategies are usually already
available. We demonstrate the computational performance of our algorithm on
flat-foot walking with the self-balanced exoskeleton Atalante.
