论文标题
形态摇摆可以帮助机器人学习
Morphological Wobbling Can Help Robots Learn
论文作者
论文摘要
我们建议在学会提高其行为绩效的同时使机器人振荡的物理特征振荡。我们考虑通常在机器人中固定的质量，执行器强度和大小等数量，并表明，当这些数量在模拟的2D软机器人的学习过程开始时振荡时，可以显着提高运动任务的性能。我们研究了该现象的动力学，并得出结论，在我们的情况下，令人惊讶的是，在很大一部分学习持续时间内，具有很大幅度的高频振荡会带来最高的绩效益处。此外，我们表明形态摇摆会显着增加对搜索空间的探索。
We propose to make the physical characteristics of a robot oscillate while it learns to improve its behavioral performance. We consider quantities such as mass, actuator strength, and size that are usually fixed in a robot, and show that when those quantities oscillate at the beginning of the learning process on a simulated 2D soft robot, the performance on a locomotion task can be significantly improved. We investigate the dynamics of the phenomenon and conclude that in our case, surprisingly, a high-frequency oscillation with a large amplitude for a large portion of the learning duration leads to the highest performance benefits. Furthermore, we show that morphological wobbling significantly increases exploration of the search space.
