Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation
Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation
复制标题
研究形态计算能力的随机最优控制方法
作者：
E. Rückert;G. Neumann
形态学计算背后的一个关键思想是，控制问题的许多困难可以通过机器人的形态学来吸收。受控系统的性能自然取决于控制架构和机器人的形态。由于这种强耦合，形态计算中的大多数令人印象深刻的应用通常应用最小控制架构。理想情况下，适应植物的形态和优化控制律相互作用，使最终，系统的最佳物理特性和最佳控制律出现。作为实现这一愿景的第一步，我们采用最优控制方法来研究形态计算的能力。我们使用概率最优控制方法来获得控制律，给定当前形态。我们表明，通过改变我们的机器人的形态，控制问题可以被简化，从而降低复杂性和更高的性能的最优控制器。这个概念是评估一个兼容的四连杆模型的人形机器人，它必须保持平衡，在外部推动的存在。
One key idea behind morphological computation is that many difficulties of a control problem can be absorbed by the morphology of a robot. The performance of the controlled system naturally depends on the control architecture and on the morphology of the robot. Because of this strong coupling, most of the impressive applications in morphological computation typically apply minimalistic control architectures. Ideally, adapting the morphology of the plant and optimizing the control law interact so that finally, optimal physical properties of the system and optimal control laws emerge. As a first step toward this vision, we apply optimal control methods for investigating the power of morphological computation. We use a probabilistic optimal control method to acquire control laws, given the current morphology. We show that by changing the morphology of our robot, control problems can be simplified, resulting in optimal controllers with reduced complexity and higher performance. This concept is evaluated on a compliant four-link model of a humanoid robot, which has to keep balance in the presence of external pushes.