Improved robotic locomotion performance through morphological computation and active control

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 been
shown 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 recent
trends, 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

FARSCOPE-TU will deliver a step change in UK capabilities in robotics and autonomous systems (RAS) by elevating technologies from niche to ubiquity. It meets the critical need for advanced RAS, placing the UK in prime position to capture a significant proportion of the estimated $18bn global market in advanced service robotics. FARSCOPE-TU will provide an advanced training network in RAS, pump priming a generation of professional and adaptable engineers and leaders who can integrate fundamental and applied innovation, thereby making impact across all the "four nations" in EPSRC's Delivery Plan. Specifically, it will have significant immediate and ongoing impact in the following six areas:
1. Training: The FARSCOPE-TU coherent strategy will deliver five cohorts trained in state-of-the-art RAS research, enterprise, responsible innovation and communication. Our students will be trained with wide knowledge of all robotics, and deep specialist skills in core domains, all within the context of the 'innovation pipeline', meeting the need for 'can-do' research engineers, unafraid to tackle new and emergent technical challenges. Students will graduate as future thought leaders, ready for deployment across UK research and industrial innovation.
2. Partner and industrial impact: The FARSCOPE-TU programme has been designed in collaboration with our industrial and end-user partners, including: DSTL; Thales; Atkins; Toshiba; Roke Manor Research; Network Rail; BT; National Nuclear Lab; AECOM; RNTNE Hospital; Designability; Bristol Heart Inst.; FiveAI; Ordnance Survey; TVS; Shadow Robot Co.; React AI; RACE (part of UKAEA) and Aimsun. Partners will deliver context and application-oriented training direct to the students throughout the course, ensuring graduates are perfectly placed to transition into their businesses and deliver rapid impact.
3. RAS community: FARSCOPE-TU will act as multidisciplinary centre in robotics and autonomous systems for the whole RAS community, provide an inclusive model for future research and training centres and bring new opportunities for networking between other centres. These include joint annual conference with other RAS CDTs and training exchanges. FARSCOPE-TU will generate significant international exposure within and beyond the RAS community, including major robotics events such as ICRA and IROS, and will interface directly with the UK-RAS network.
4. Societal Impact: FARSCOPE-TU will promote an informed debate on the adoption of autonomous robotics in society, cutting through hype and fear while promoting the highest levels of ethics and safety. All students will design and deliver public engagement events to schools and the public, generating knock-on impact in two ways: greater STEM uptake enhances future economic potential, and greater awareness makes people better users of robots, amplifying societal benefits.
5. Economic impact: FARSCOPE-TU will not only train cohorts in fundamental and applied research but will also demonstrate how to bridge the "technology valley of death" between lower and higher TRL. This will enable students to exploit their ideas in technology incubators (incl. BRL incubator, SetSquared and EngineShed) and through IP protection. FARSCOPE-TU's vision of ubiquitous robotics will extend its impact across all UK industrial and social sectors, from energy suppliers, transport and agriculture to healthcare, aging and human-machine interaction. It will pump-prime ubiquitous UK robotics, inspiring and enabling myriad new businesses and economic and social impact opportunities.
6. Long-term Impact: FARSCOPE-TU will have long-term impact beyond the funded lifetime of the Centre through a network for alumni, enabling knowledge exchange and networking between current and past students, and with partners and research groups. FARSCOPE-TU will have significant positive impact on the 80-strong non-CDT postgraduate student body in BRL, extending best-practice in supervision and training.