Classic control theory applied to compliant and soft robots generally involves an increment of computation that has no equivalent in biology. To tackle this, morphological computation describes a theoretical framework that takes advantage of the computational capabilities of physical bodies. However, concrete applications in robotic locomotion control are still rare. Also, the trade-off between compliance and the capacity of a physical body to facilitate its own control has not been thoroughly studied in a real locomotion task. In this paper, we address these two problems on the state-of-the-art hydraulic robot HyQ. An end-to-end neural network is trained to control HyQ’s joints positions and velocities using only Ground Reaction Forces. Our simulations and experiments demonstrate better controllability using less memory and computational resources when increasing compliance. However, we show empirically that this effect cannot be attributed to the ability of the body to perform intrinsic computation. It invites to give an increased emphasis on compliance and co-design of the controller and the robot to facilitate attempts in machine learning locomotion.

应用于顺应性和软机器人的经典控制理论通常涉及到生物学上没有等效计算量的增量。为了解决这个问题，形态计算描述了一种理论框架，该框架利用了物理物体的计算能力。但是，在机器人运动控制中的具体应用仍然很少。而且，在实际的运动任务中还没有彻底研究顺应性与物理身体促进自身控制的能力之间的权衡。在本文中，我们将在最先进的液压机器人HyQ上解决这两个问题。端到端神经网络经过训练，仅使用地面反作用力即可控制HyQ的关节位置和速度。我们的仿真和实验表明，在提高合规性时，使用较少的内存和计算资源即可获得更好的可控性。但是，我们凭经验表明，这种影响不能归因于人体进行内在计算的能力。它要求更加强调控制器和机器人的合规性和协同设计，以促进机器学习运动的尝试。