用简单方法实现四足与人形机器人全身实时模型预测控制。
Whole-Body Model-Predictive Control of Legged Robots with MuJoCo
- 基于MuJoCo物理引擎和iLQR算法，实现全身运动规划。
- 在真实硬件上实现实时控制，支持动态四足行走与全尺寸人形双足行走。
- 代码开源，便于复现，降低真实场景研究门槛。
我们展示了在四足和人形机器人上，一种非常简单的全身模型预测控制（MPC）方法在真实世界中的惊人效果：采用MuJoCo动力学模型与有限差分近似导数的迭代LQR（iLQR）算法。基于先前在仿真中使用MuJoCo成功实现步态与操作任务建模与控制的经验，我们发现这些策略只需极少的模拟到现实迁移调整即可在真实环境中有效运行。该基线方法在多种硬件实验中实现了实时全身MPC，包括动态四足步态、两腿站立行走及全尺寸人形双足行走。我们希望这一易于复现的硬件基准能降低真实世界全身MPC研究的门槛，推动社区研究速度提升。代码与实验视频将公开于：https://johnzhang3.github.io/mujoco_ilqr。
原文摘要 · Abstract (English)
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
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