2026
RL-Augmented MPC for Non-Gaited Legged and Hybrid Locomotion
Andrea Patrizi, Carlo Rizzardo, Arturo Laurenzi, Francesco Ruscelli, Luca Rossini, Nikos G. Tsagarakis
RA-L 2026
We propose a contact-explicit hierarchical architecture coupling Reinforcement Learning (RL) and Model Predictive Control (MPC), where a high-level RL agent provides gait and navigation commands to a low-level locomotion MPC. This off loads the combinatorial burden of contact timing from the MPC by