Cost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid Locomotion

• URL: http://arxiv.org/abs/2603.28243v1
• Date: Mon, 30 Mar 2026 10:05:15 GMT
• Title: Cost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid Locomotion
• Abstract summary: We propose a cost-matching approach for optimal humanoid locomotion within a Model Predictive Control (MPC)-based Reinforcement Learning framework.<n>A parameterized MPC formulation with centroidal dynamics is trained to approximate the action-value function obtained from high-fidelity closed-loop data.<n>Results demonstrate improved locomotion performance and robustness to model mismatch and external disturbances.
• Score: 15.125805248577278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a cost-matching approach for optimal humanoid locomotion within a Model Predictive Control (MPC)-based Reinforcement Learning (RL) framework. A parameterized MPC formulation with centroidal dynamics is trained to approximate the action-value function obtained from high-fidelity closed-loop data. Specifically, the MPC cost-to-go is evaluated along recorded state-action trajectories, and the parameters are updated to minimize the discrepancy between MPC-predicted values and measured returns. This formulation enables efficient gradient-based learning while avoiding the computational burden of repeatedly solving the MPC problem during training. The proposed method is validated in simulation using a commercial humanoid platform. Results demonstrate improved locomotion performance and robustness to model mismatch and external disturbances compared with manually tuned baselines.

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