Improved robotic locomotion performance through morphological computation and active control

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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