基于成本匹配的模型预测控制用于人形机器人高效强化学习
Cost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid Locomotion
AI总结：
本文提出一种基于模型预测控制的强化学习框架中的成本匹配方法，通过参数化MPC和中心动力学来近似高保真闭环数据获得的动作价值函数，提升人形机器人运动性能和鲁棒性。
摘要：
本文提出了一种基于模型预测控制（MPC）的强化学习框架中的成本匹配方法，用于最优的人形机器人运动控制。通过参数化MPC公式和中心动力学，训练以近似高保真闭环数据获得的动作价值函数。具体而言，沿着记录的状态-动作轨迹评估MPC的费用到目标，并更新参数以最小化MPC预测值与测量回报之间的差异。该方法在训练过程中避免了重复求解MPC问题的计算负担，通过商业人形平台在仿真中验证。结果表明，与手动调优的基线相比，运动性能和对模型不匹配和外部扰动的鲁棒性有所提高。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
