论文标题
定制的正弦波足以模仿不同样式的双足步骤学习
Custom Sine Waves Are Enough for Imitation Learning of Bipedal Gaits with Different Styles
论文作者
论文摘要
直到最近，通过增强学习实现了强大的双足动力。但是，现有的实施很大程度上依赖于人类专家的见解和努力，这对于机器人系统的迭代设计来说是昂贵的。同样，学到的运动的样式严格限于参考。在本文中，我们提出了一种从简单的正弦波中学习两足球运动的新方法，作为脚高度的参考。有了天真的人类洞察力，即应替代和定期提起两英尺，我们在Cassie机器人上进行了实验证明，简单的奖励功能能够使机器人学会在没有任何模型的明确知识的情况下端到端，有效地行走。使用自定义的正弦波，学习的步态图案也可以具有自定义样式。代码在github.com/wooqi57/sin-cassie-rl上发布。
Not until recently, robust bipedal locomotion has been achieved through reinforcement learning. However, existing implementations rely heavily on insights and efforts from human experts, which is costly for the iterative design of robot systems. Also, styles of the learned motion are strictly limited to that of the reference. In this paper, we propose a new way to learn bipedal locomotion from a simple sine wave as the reference for foot heights. With the naive human insight that the two feet should be lifted up alternatively and periodically, we experimentally demonstrate on the Cassie robot that, a simple reward function is able to make the robot learn to walk end-to-end and efficiently without any explicit knowledge of the model. With custom sine waves, the learned gait pattern can also have customized styles. Codes are released at github.com/WooQi57/sin-cassie-rl.
