We demonstrate the surprising real-world effectiveness of a very simple approach to whole-body model-predictive control (MPC) of quadruped and humanoid robots: the iterative LQR (iLQR) algorithm with MuJoCo dynamics and finite-difference approximated derivatives. Building upon the previous success of model-based behavior synthesis and control of locomotion and manipulation tasks with MuJoCo in simulation, we show that these policies can easily generalize to the real world with few sim-to-real considerations. Our baseline method achieves real-time whole-body MPC on a variety of hardware experiments, including dynamic quadruped locomotion, quadruped walking on two legs, and full-sized humanoid bipedal locomotion. We hope this easy-to-reproduce hardware baseline lowers the barrier to entry for real-world whole-body MPC research and contributes to accelerating research velocity in the community. Our code and experiment videos will be available online at:https://johnzhang3.github.io/mujoco_ilqr

翻译：我们展示了一种非常简单的四足和人形机器人全身模型预测控制（MPC）方法在现实世界中的惊人有效性：该方法采用迭代LQR（iLQR）算法，结合MuJoCo动力学模型和有限差分近似导数。基于先前在仿真中使用MuJoCo成功实现基于模型的行为合成以及运动与操作任务控制的研究，我们证明这些控制策略能够轻松迁移至现实世界，且仅需极少的仿真到现实适应考量。我们的基准方法在多种硬件实验中实现了实时全身MPC，包括动态四足运动、四足机器人双足行走以及全尺寸人形双足运动。我们希望这一易于复现的硬件基准能够降低现实世界全身MPC研究的入门门槛，并为加速该领域的研究进程做出贡献。我们的代码与实验视频将发布于：https://johnzhang3.github.io/mujoco_ilqr