Problem
Research questions and friction points this paper is trying to address.
bipedal locomotion
closed-loop control
linear-quadratic regulator
reliable walking
Closing the Loop on the Poppy Humanoid: Bipedal Locomotion with Linear-Quadratic Control and Learned Cost Functions
🤖 AI Summary
本文解决了Poppy机器人稳定双足行走问题，通过线性二次调节器框架和学习成本函数的方法实现了闭环控制。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Linear-Quadratic Regulator (LQR)
Closed-Loop Control
Bipedal Locomotion
Learned Cost Function
🔎 Similar Papers
No similar papers found.
