发表机构
Syracuse University（雪城大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对开源低成本的Poppy人形机器人，提出结合线性二次调节器（LQR）与学习二次代价函数的闭环行走控制器，经实验验证其行走性能较开环轨迹回放有统计学意义的显著提升。
AI 中文摘要
Poppy人形机器人是一款开源低成本机器人，适用于人工智能领域的研究与教育，但目前尚无公开方法能在标准Poppy硬件上实现可靠的无辅助双足运动。本文提出一种基于线性二次调节器（LQR）轨迹跟踪框架的功能型闭环行走控制器，通过标称行走轨迹开环回放收集的数据，为LQR控制器学习二次代价函数，大幅提升运动可靠性。经验证，该闭环控制器与开环轨迹回放相比，行走性能有统计学意义的显著提升。
英文摘要
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.
CommentsAccepted by International Conference on the AI Revolution: Research, Ethics, and Society (AIR-RES 2026)
