Improved robotic locomotion performance through morphological computation and active control

多年来，有腿运动的机器人表现出高度的灵活性、优异的动态稳定性和对不同地形和障碍物的适应性。它们在不同的环境中表现出了这些特点，特别是在救援、侦察、保健、安全和海洋环境等领域。机器人在涉及阴暗、肮脏和危险环境的应用中已被证明具有优势，而核退役等重要任务仍然是国家的关键优先事项。人们普遍认为，超过50%的地球表面无法进入轮子或轨道[2]，从最近的商业成功（如ANYbotics[3]）和具有成本效益的开源系统（如Stanford Doggo[4]和ODRI[5]）来看，有腿的机器人将发挥越来越大的作用。尽管腿式机器人的发展证明了安全性，鲁棒性和高性能，但很明显，商用机器人设计具有集中控制，每个关节都是驱动的。在这里，有必要介绍形态计算（MC），它在具身（人工）智能的背景下，指的是由身体（和环境）执行的过程，否则将不得不由大脑执行。MC与生物和机器人系统的研究相关，如被动动态步行者[7]所示，这是一个纯机械系统。被动动态步行者表明，在没有驱动的情况下，行走可以由系统的物理特性和环境的相互作用产生。在机器人技术中，这意味着具有高形态计算的系统只需要在需要时生成运动命令。这样的控制方案不仅增加了系统的耐用性（因为执行器的磨损减少了），还意味着具有高MC的机器人将减少其驱动bb0的能量需求。这对有腿的机器人很有用，因为它们需要不受束缚才能自主运作，并在具有挑战性的地形上运输有效载荷。虽然MC在生物系统中的作用得到了很好的接受，但Ghazi-Zahediet等人在他们对最近趋势的探索中指出，它在机器人技术中的应用仍未得到充分的探索。作者强调的一个主要原因是，传统的控制范式将身体视为需要控制的东西，而不是用作计算资源。通过使用控制系统和伺服电机，有一种抑制任何不良形态行为的趋势，如非线性、欠驱动或噪声。值得注意的是，这些相同的复杂形态特性在自然系统的行为中起着关键作用，正如Abad等人在减少滑移[9]和小狗[7]（一个欠驱动机器人）的山羊蹄MC中所描述的那样。与此同时，Deimal等人提出的挑战之一是确保系统利用形态（“好MC”），同时避免有害的身体-环境相互作用（“坏MC”）。为了实现自动控制与主动控制的平衡，需要一种形式化的方法来测量系统中的自动控制。Ghazi-Zahedi等人在详细介绍算法的论文中展示了两种测量MC的方法，其中一种是将行为复杂度与控制器复杂度进行比较，前者通过世界状态信息，后者通过传感器状态信息。通过对当前学术界和工业界的回顾得出的结论是，利用计算能力、集中控制和形态特征的相互作用，在不同的应用中更加节能和通用的机器人系统具有巨大的潜力

Robots with legged locomotion have, over the years, demonstrated high flexibility, excellent dynamic stability and adaptability to different terrains and obstacles [1]. They have demonstrated these characteristics in different environments, especially in areas such as rescue, reconnaissance, health-care, security and marine environments [2]. Robots have beenshown to be advantageous in applications involving dull, dirty and dangerous environments, and vital tasks such as nuclear decommissioning remain a key national priority. It is widely believed that over 50% of the earth's surface is inaccessible to wheels or tracks [2], and legged robots have an increasing role to play, judging by recent commercial successes such as ANYbotics [3] and cost-effective, open source systems such as Stanford Doggo [4] and the ODRI [5].Despite the development of legged robots demonstrating safety, robustness and high performance, it is evident that the commercially available robots are designed with centralised control, with every joint actuated. Here, it is pertinent to introduce Morphological Computation (MC), which, in the context of embodied (artificial) intelligence, refers to processes, which are conducted by the body (and environment) that otherwise would have to be performed by the brain [6]. MC is relevant in the study of biological and robotic systems as illustrated in the Passive Dynamic Walker [7], which is a purely mechanical system. The Passive Dynamic Walker shows that walking can result from the interaction of the system's physical properties and its environment without actuation. In the context of robotics, this means that systems with high morphological computation only need to generate motor commands when they are needed. Not only does such a control scheme increase the durability of the systems (because the wear-out of the actuators is decreased), it also means that robots with high MC will have a reduced energy demand for their actuation [8]. This is useful for legged robots, since they need to be untethered for autonomous functioning and transporting payloads over challenging terrains. While there is good acceptance of the role of MC in biological systems, Ghazi-Zahediet al [8], in their exploration on recenttrends, state that its application in robotics remains underexplored. One of the main reasons emphasised by the authors is that the conventional control paradigms treat the body as something that needs to be dominated rather than being used as a computing resource. There is a tendency to suppress any undesirable morphological behaviours like nonlinearity, underactuation or noise through the use of control systems and servo motors. It is quite remarkable that these same complex morphological properties play a key role in the behaviour of natural systems, as described in the research by Abad et al in the MC of a goat hoof in slip reduction [9] and the Puppy [7], an under-actuated robot. At the same time, one of the challenges set out by Deimal et al [10] is to ensure that the systems take advantage of the morphology ('good MC') while avoiding harmful body-environment interactions with respect to the desired functionality ('bad MC'). To achieve the right balance of MC and active control, a formal method is required to measure MC in the system. In their paper detailed with algorithms, Ghazi-Zahedi et al [6] have demonstrated two methods of measuring MC, one of which is to compare behaviour complexity with controller complexity, the former by information of world states and the latter by information of sensor states. The conclusion from review of current landscape of academia and industry is that there is a huge potential for robotics systems that use the inter-play of computational power, centralised control, and morphological features to be more energy efficient and versatile across different applications