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In legged logomotion, online trajectory optimization techniques generallydepend on heuristic-based contact planners in order to have low computationtimes and achieve high replanning frequencies. In this work, we proposeContactNet, a fast acyclic contact planner based on a multi-output regressionneural network. ContactNet ranks discretized stepping regions, allowing toquickly choose the best feasible solution, even in complex environments. Thelow computation time, in the order of 1 ms, makes possible the execution of thecontact planner concurrently with a trajectory optimizer in a Model PredictiveControl (MPC) fashion. We demonstrate the effectiveness of the approach insimulation in different complex scenarios with the quadruped robot Solo12.

ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion