用LQR和学习成本函数，让低成本人形机器人稳定走路
Closing the Loop on the Poppy Humanoid: Bipedal Locomotion with Linear-Quadratic Control and Learned Cost Functions
- 基于LQR框架，从开环数据学习最优成本函数
- 闭环控制使行走可靠性显著提升，统计上优于开环播放
- 适合机器人控制、教育与低预算研究者参考
Poppy人形机器人是开源且低成本的平台，适用于人工智能研究与教育。然而，目前尚无公开方法能在标准硬件上实现可靠、无需辅助的双足行走。本文提出一种闭环步行控制器，基于线性二次型调节器（LQR）框架进行轨迹跟踪。通过采集开环播放标准步态轨迹的数据，所提方法学习了一个二次型成本函数，显著提升了运动可靠性。实验验证表明，该闭环控制器在行走性能上相比开环轨迹播放具有统计学意义的提升。
原文摘要 · Abstract (English)
The Poppy Humanoid is an open-source, low-cost robot suitable for research and education in artificial intelligence. However, we are unaware of any published methodology that achieves reliable, unassisted bipedal locomotion on the standard Poppy hardware. This paper contributes a functional closed-loop walking controller for Poppy, based on the linear-quadratic regulator (LQR) framework for trajectory tracking. Starting with data collected from open-loop playback of a nominal walking trajectory, our proposed method learns a quadratic cost function for an LQR controller that substantially improves the reliability of the motion. The closed-loop controller is validated empirically, demonstrating statistically significant improvements in walking performance compared to open-loop trajectory playback.
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