用学习的零动力学策略，让不完全驱动系统更稳定敏捷。
Robust Agility via Learned Zero Dynamics Policies
- 通过学习不变映射，将控制问题降维到不可直接驱动的自由度上。
- 3000次跳跃中稳定抗扰，粗糙地形上表现优异。
- 适合做机器人跳跃、平衡等复杂动态控制任务的研究者。
本文研究混合不完全驱动系统的鲁棒敏捷控制器设计。提出的方法将稳定控制器设计分为两步：1）学习一个在最优控制下保持不变的映射；2）将可驱动坐标引导至该映射的输出。该方法称为零动力学策略（Zero Dynamics Policies），通过将目标映射的输入限制在无法直接驱动的自由度子集上，利用不完全驱动结构实现显著降维。同时保留了最优控制的稳定性与约束满足性，并大幅降低在线计算开销。理论证明此类控制器可稳定混合不完全驱动系统，实验在3D跳跃平台ARCHER上验证，3000次跳跃过程中表现出鲁棒敏捷性，在粗糙地形上有效抵抗干扰。
原文摘要 · Abstract (English)
We study the design of robust and agile controllers for hybrid underactuated systems. Our approach breaks down the task of creating a stabilizing controller into: 1) learning a mapping that is invariant under optimal control, and 2) driving the actuated coordinates to the output of that mapping. This approach, termed Zero Dynamics Policies, exploits the structure of underactuation by restricting the inputs of the target mapping to the subset of degrees of freedom that cannot be directly actuated, thereby achieving significant dimension reduction. Furthermore, we retain the stability and constraint satisfaction of optimal control while reducing the online computational overhead. We prove that controllers of this type stabilize hybrid underactuated systems and experimentally validate our approach on the 3D hopping platform, ARCHER. Over the course of 3000 hops the proposed framework demonstrates robust agility, maintaining stable hopping while rejecting disturbances on rough terrain.
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