  In legged logomotion, online trajectory optimization techniques generally
depend on heuristic-based contact planners in order to have low computation
times and achieve high replanning frequencies. In this work, we propose
ContactNet, a fast acyclic contact planner based on a multi-output regression
neural network. ContactNet ranks discretized stepping regions, allowing to
quickly choose the best feasible solution, even in complex environments. The
low computation time, in the order of 1 ms, makes possible the execution of the
contact planner concurrently with a trajectory optimizer in a Model Predictive
Control (MPC) fashion. We demonstrate the effectiveness of the approach in
simulation in different complex scenarios with the quadruped robot Solo12.
