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

The field of legged robots has seen tremendous progress in the last few years. Locomotion trajectories are commonly generated by optimization algorithms in a Model Predictive Control (MPC) loop. To achieve online trajectory optimization, the locomotion community generally makes use of heuristic-based contact planners due to their low computation times and 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 locations, allowing to quickly choose the best feasible solution, even in complex environments. The low computation time, in the order of 1 ms, enables the execution of the contact planner concurrently with a trajectory optimizer in a MPC fashion. In addition, the computational time does not scale up with the configuration of the terrain. We demonstrate the effectiveness of the approach in simulation in different scenarios with the quadruped robot Solo12. To the best knowledge of the authors, this is the first time a contact planner is presented that does not exhibit an increasing computational time on irregular terrains with an increasing number of gaps.