Many assembly and disassembly tasks in manufacturing have small clearances and limited accessibility, such as shaft-hole insertion/separation and bolt-nut assembly/disassembly. Using robots in these contact-rich tasks is more complex than those having no physical contacts (e.g. computer visual inspection) or simple contacts (e.g. cutting, welding, pick-and-place). The deployment of robots in contact-rich tasks has been limited to date. The contact-rich tasks that involve complex shapes, small clearances or deformable materials are particularly challenging to robotise due to the likely events of jamming and wedging. Our previous research has investigated techniques that allow robots to learn contact-rich skills (e.g. complex motion plans and force control policies) using two main AI-based pathways: (1) self-learning from trial-and-error, and (2) learning from human demonstrations. The two participating universities, Birmingham and Sheffield, have research experiences in (1) and (2), respectively. A key challenge observed in the current research is that in many cases a robot's contact-rich skill cannot be performed by other robots of different motion properties (e.g. accuracy, precision and stiffness), or be applied to a new task with variations (e.g. differences in object geometry, shape, and materials). This is because a robotic contact-rich skill, i.e. control policies and motion plans, is usually acquired for a specific task and cannot be adopted by new robots or in new tasks. STAMAN's vision is to create AI-based mechanisms to allow robots to share and recreate obtained digital skills (e.g. motion and force/torque control strategies) to allow easy automation scale-up for contact-rich tasks. This includes considering two research questions: 1) For skill transfer - how can a contact-rich skill be quickly transferred to a different robot (e.g. transferring a bolt-nut separation skill from a high-precision robot to a low-precision robot)? 2) For skill augmentation - how can existing contact-rich skills be used to create new contact-rich skills (e.g. augmentation of rigid-material skills to deal with soft materials)? The project will develop a portfolio of research into the science of digital skills for contact-rich tasks, focusing on common manufacturing tasks such as bolt-nut assembly/disassembly, peg-hole insertion/separation, and shaft-ring assembly/disassembly. The ability to transfer and augment digital skills for contact-rich tasks will allow automation systems to be implemented on a larger scale, with minimal manual setting and fine-tuning required. STAMAN aims to create transferrable and augmentable digital skills that will underpin the development of mass machine skills for future manufacturing, similar to how industrial robots have contributed to modern mass production. The proposed research encourages more use of robots in assembly (e.g. automotive, aerospace, electronics, etc.) and disassembly (e.g. repairs, remanufacturing and recycling), and thus directly contributes to the UK's Made Smarter initiative and the circular economy goals.

In the current Circular Economy (CE) landscape, many activities fail to fully realise the potential value of products, components, and materials. Rather than being repaired, reused, or remanufactured, a significant number of products end up in mixed waste streams and are recycled, which is the least valuable end-of-life CE option. This leads to a substantial loss of residual value. One of the critical challenges in the CE is the absence of effective and efficient methods for large-scale separation of products, which are in very different used or end-of-life conditions, into various CE options such as reuse, repair, remanufacture, repurpose, or recycle. The presence of mixed waste streams poses a significant barrier to creating tighter loops of circularity and preserving materials at their highest value for an extended period. The current methods of evaluating end-of-life options are inefficient and inaccurate as they heavily rely on human judgment, which is often based on experience and prone to bias and error. Similar to how triage can help create priorities and organisations in healthcare systems, RoboTriage proposes a new concept, circularity triage, referring to the process of rapidly examining products, components and parts to determine their best CE option, e.g. reuse, repair, remanufacture, repurpose, or recycle. We aim to create and develop robotic systems that can perform circularity triage by capturing the health condition data of used products, allowing for a swift evaluation of product conditions so as to group products of similar conditions, avoid mixed waste streams and recommend the highest-value CE options. RoboTriage has five objectives: O1: To create robotic systems that can perform triage operations (at the operation level). O2: To create system intelligence that enables smart planning of triage operations (task level). O3: To identify new patterns and connections between product history data and product conditions using autonomous large-scale robotic triage (task level). O4: To identify value opportunities and develop circular business models with the new patterns and connections obtained to facilitate high-value retention (system level). O5: To support the uptake of CE and sustainability considerations and practices by industrial partners through three flagship case studies. RoboTriage's academic impact transcends the manufacturing and CE domains, extending to ICT, AI, and data science. The influence of RoboTriage extends into economic, societal, and environmental domains. RoboTriage technologies have the potential to be deployed for CE purposes, thereby enhancing their scale and productivity. On average, a one per cent increase in robot density correlates with a 0.8 per cent increase in productivity. The impact of ideas in RoboTriage will be exemplified by our 11 industrial partners and over £700k contributions (including over £400k cash contributions) from the three host institutions and external partners. Facilitated by RoboTriage technologies, the promotion of high-value CE options such as remanufacturing could lead to a 90% reduction in primary material usage and a 55% reduction in energy and emission impact. The impact of this project will extend to international organisations through our United Nations partners, ITU and UNESCO, both of which are also our project partners.