论文标题
混合ILQR模型模型预测控制，用于接触机器人的接触隐式稳定
Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots
论文作者
论文摘要
模型预测控制（MPC）是控制机器人的流行策略，但由于混合动力学的复杂性质，很难接触系统。为了实现具有联系的系统的MPC，通常会简化动态模型或及时固定的接触序列，以便有效地计划轨迹。在这项工作中，我们通过1）将混合迭代线性二次调节器扩展到以MPC方式（HILQR MPC）工作，以1）进行1）修改成本函数时如何计算当接触模式不一致时，2）使用并行在模拟刚性身体动态时使用并行处理，以及3）使用有效的分析性分析衍生衍生的计算较差的身体动力学。结果是一个可以修改参考行为的接触序列并掌握整个身体运动的系统 - 在处理大型扰动时至关重要。 HILQR MPC在两个系统上进行了测试：首先，在简单的弹力弹跳球混合系统上验证了混合成本修改。然后，将HILQR MPC与在四倍的机器人（Unitree A1）上使用质心动态假设的方法进行了比较。 HILQR MPC在模拟和硬件测试中的表现优于质心方法。
Model Predictive Control (MPC) is a popular strategy for controlling robots but is difficult for systems with contact due to the complex nature of hybrid dynamics. To implement MPC for systems with contact, dynamic models are often simplified or contact sequences fixed in time in order to plan trajectories efficiently. In this work, we extend Hybrid iterative Linear Quadratic Regulator to work in a MPC fashion (HiLQR MPC) by 1) modifying how the cost function is computed when contact modes do not align, 2) utilizing parallelizations when simulating rigid body dynamics, and 3) using efficient analytical derivative computations of the rigid body dynamics. The result is a system that can modify the contact sequence of the reference behavior and plan whole body motions cohesively -- which is crucial when dealing with large perturbations. HiLQR MPC is tested on two systems: first, the hybrid cost modification is validated on a simple actuated bouncing ball hybrid system. Then HiLQR MPC is compared against methods that utilize centroidal dynamic assumptions on a quadruped robot (Unitree A1). HiLQR MPC outperforms the centroidal methods in both simulation and hardware tests.
