Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments
Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments
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DOI：
10.1109/icra48891.2023.10161302
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Mingyo Seo;Ryan Gupta;Yifeng Zhu;Alexy Skoutnev;L. Sentis;Yuke Zhu
中科院分区：
文献类型：
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作者：
Mingyo Seo;Ryan Gupta;Yifeng Zhu;Alexy Skoutnev;L. Sentis;Yuke Zhu
我们解决了动态环境中感知运动的问题。在这个问题中，一个四足机器人必须表现出强大的和敏捷的行走行为，以应对环境的混乱和移动的障碍。我们提出了一个分层的学习框架，名为PRELUDE，它分解的感知运动的问题，高层决策预测导航命令和低层的步态生成，以实现目标命令。在这个框架中，我们训练高层次的导航控制器与模仿学习上收集的可操纵的手推车和低层次的步态控制器与强化学习（RL）的人类示范。因此，我们的方法可以获得复杂的导航行为从人类的监督和发现多才多艺的步态从试错。我们证明了我们的方法在仿真和硬件实验的有效性。视频和代码可以在项目页面上找到：https://ut-austin-rpl.github.io/PRELUDE。
We tackle the problem of perceptive locomotion in dynamic environments. In this problem, a quadrupedal robot must exhibit robust and agile walking behaviors in response to environmental clutter and moving obstacles. We present a hierarchical learning framework, named PRELUDE, which decomposes the problem of perceptive locomotion into high-level decision-making to predict navigation commands and low-level gait generation to realize the target commands. In this framework, we train the high-level navigation controller with imitation learning on human demonstrations collected on a steerable cart and the low-level gait controller with reinforcement learning (RL). Therefore, our method can acquire complex navigation behaviors from human supervision and discover versatile gaits from trial and error. We demonstrate the effectiveness of our approach in simulation and with hardware experiments. Videos and code can be found at the project page: https://ut-austin-rpl.github.io/PRELUDE.