The role of feedback in morphological computation with compliant bodies
The role of feedback in morphological computation with compliant bodies
复制标题
DOI：
10.1007/s00422-012-0516-4
复制
发表时间：
2012-11-01
影响因子：
1.9
通讯作者：
Maass, Wolfgang
中科院分区：
文献类型：
--
作者：
Hauser, Helmut;Ijspeert, Auke J.;Maass, Wolfgang
复杂的非线性机器人系统的鲁棒周期运动的产生是固有的困难，特别是，如果机器人的部分是符合。以前有人提出，机器人的复杂非线性特征，类似于生物有机体，可能有助于其控制。这种大胆的假设，通常被称为形态计算，最近得到了豪瑟等人的一些理论支持（Biol Cybern 105：355-370，doi：10.1007/s 00422 -012-0471-0，2012）。我们在这篇文章中表明，这种理论支持可以扩展到不仅覆盖对外部信号的衰减记忆响应的情况，而且还覆盖自适应周期性模式的自主生成的基本情况，例如，运动所需要的。该理论预测，反馈到形态计算系统是必要的和足够的任务，其中褪色的记忆是不够的。我们证明了这种理论分析的可行性，通过计算机模拟的复杂的非线性质量弹簧系统，训练产生一个大的多样性的周期性运动，通过适应一个简单的线性反馈装置的权重。因此，本文的研究结果大大扩展了形态计算在机器人学中理论上易于处理的应用领域，也为理解生物有机体的控制原理提供了新的范例。
The generation of robust periodic movements of complex nonlinear robotic systems is inherently difficult, especially, if parts of the robots are compliant. It has previously been proposed that complex nonlinear features of a robot, similarly as in biological organisms, might possibly facilitate its control. This bold hypothesis, commonly referred to as morphological computation, has recently received some theoretical support by Hauser et al. (Biol Cybern 105:355-370, doi:10.1007/s00422-012-0471-0, 2012). We show in this article that this theoretical support can be extended to cover not only the case of fading memory responses to external signals, but also the essential case of autonomous generation of adaptive periodic patterns, as, e.g., needed for locomotion. The theory predicts that feedback into the morphological computing system is necessary and sufficient for such tasks, for which a fading memory is insufficient. We demonstrate the viability of this theoretical analysis through computer simulations of complex nonlinear mass-spring systems that are trained to generate a large diversity of periodic movements by adapting the weights of a simple linear feedback device. Hence, the results of this article substantially enlarge the theoretically tractable application domain of morphological computation in robotics, and also provide new paradigms for understanding control principles of biological organisms.