A digital COgnitive architecture to achieve Rapid Task programming and flEXibility in manufacturing robots through human demonstrations (DIGI-CORTEX)

该报告指出，英国在工人生产率方面落后，可以从新型智能技术、互联设备、机器人和人工智能等新型工业数字工具（idt）的出现中受益。据估计，到2035年，IDTs将为英国经济额外贡献6300亿英镑，并使制造业每年增长1.5%至3%。例如，对定制和个性化商品的需求量很大。为了满足这一需求，制造系统需要高度灵活、适应性强和高度自动化。由于大多数制造业中小企业利用jobshop并贡献了英国经济的15% b[3]，为他们配备能够快速灵活地学习任务的机器人（类似于人类如何快速训练以组装新产品线）将使中小企业能够满足高订单需求，从而改善英国PLC的出口机会和英国的GDP。该提案旨在研究认知架构，通过被动观察人类向机器人演示任务，并将先前学习的技能应用于新的任务场景，从而使机器人具备快速学习新技能的能力，从而在制造车间实现任务灵活性。这开启了令人兴奋的可能性。首先，这意味着机器人可以被教导完成各种任务，而不需要人类进行密集的编程。这也意味着机器人可以灵活地用于执行各种各样的任务，从而减少对资本密集型、刚性和耗时的制造设置的需求。将数字心智模型应用到机器人上，以构建灵活和创造性的机器人，这些机器人可以灵活和快速地重新执行各种任务，在文献中存在空白。然而，人们越来越认识到，未来的工业机器人需要创造力，而这可以通过为它们提供心智模型来实现。本文首次研究了一种认知架构，该架构将人类在制造机器人创造性问题解决和任务结构映射方面的认知能力嵌入到一个统一的框架中，目的是通过被动的人类演示实现工业机器人的快速重任务（任务灵活性）。最先进的架构（如SOAR和art - r）经常使用预先任务通知的严格程序规则，这使得它们不太适合探索机器人的快速重新任务，而使用机器学习范式（如深度神经网络或强化学习）的技术需要大量数据并导致任务特定的应用程序。此外，这些技术尚未成功地与通过设想创建数字心智模型相结合，并应用于类似于本提案中要研究的制造环境中的各种任务。总而言之，该提案的新颖之处在于应用机器人设想的数字心智模型，以支持他们在制造业（以及制造业以外的其他部门）遇到的问题的形态学解决方案的创造力和想象力，以及支持将以前学过的技能应用于新的类似任务。这将导致机器人快速重新分配任务和任务灵活性。参考文献：[1]J. Maier，“Made Smarter Review”，2017D. Brown， A. Swift，和E. Smart，“数据分析和决策制定”，朴茨茅斯大学研究所，第1- 20,2019,doi: 10.4324/9781315743011-9.[3]C. Rhodes，《商业统计》，2019J. B. Hamrick，“深度学习中心理模拟和想象的类似物”，《当代观点行为科学》，第29卷，第8-16页，2019。

The Made Smarter review identified that the UK is lagging behind in worker productivity and could benefit from the advent of new industrial digital tools (IDTs) such as novel intelligent technologies, connected devices, robotics and artificial Intelligence. It is estimated that IDTs could contribute an additional £630bn to the UK economy by 2035 and increase the manufacturing sector growth by between 1.5 and 3% per annum [1]. For example, there is a high demand for bespoke and personalised goods in high volume [2]. In order to meet this demand, manufacturing systems need to be highly flexible, adaptable and highly automated. Since most manufacturing SMEs make use of jobshops and contribute up to 15% of the UK economy [3], equipping them with robots that can learn a task rapidly and flexibly (similar to how a human can be rapidly trained to assemble new product lines) will enable SMEs to meet high order demands thereby improving UK PLC's export opportunities and UK's GDP. This proposal aims to investigate cognitive architectures that equips robots with the capability to rapidly learn new skills by passive observation of a human demonstrating a task to the robot and applying previously learnt skills to new task scenarios, thereby achieving task flexibility on the manufacturing floor. This opens up exciting possibilities. For one, it means that robots can be taught to do various tasks with no intensive programming required by a human. It also means that robots can be flexibly used to perform a wide variety of tasks thereby reducing the need for capital intensive, rigid and time-consuming manufacturing set ups.There is a gap in literature of applying digital mental models on robots for building in flexible and creative robots that can be flexibly and rapidly re-tasked for various tasks. Nevertheless, there is a growing realisation that creativity is needed in industrial robots of the future and that this could be achieved through providing them with mental models [4]. For the first time ever, this proposal investigates a cognitive architecture that embeds the human cognitive capabilities of mental simulation for creative problem solving on manufacturing robots and task structure mapping in a unified framework for the purposes of achieving rapid re-tasking (task flexibility) of industrial robots via passive human demonstrations. State of the art architectures (such as SOAR and ART-R) often make use of a prior task informed rigid procedural rules that make them less amenable for exploring rapid re-tasking on robots while techniques that use machine learning paradigms (e.g deep neural networks or reinforcement learning) that require lots of data and result in task specific applications. Furthermore, these techniques are yet to be successfully combined with the creation of digital mental models through envisioning and applied to varying tasks in manufacturing environments similar to those to be investigated in this proposal. In summary, the novelty of this proposal is in the application of robot envisioned digital mental models to support them in creativity and imagination of morphological informed solutions to problems encountered in manufacturing (and other sectors outside manufacturing) as well as to support the application of previously learnt skills to new similar tasks. This will lead to rapid re-tasking and task flexibility in robots. References:[1] J. Maier, "Made Smarter Review," 2017.[2] D. Brown, A. Swift, and E. Smart, "Data analytics and decision making," Inst. Ind. Res. Univ. Portsmouth, pp. 1-20, 2019, doi: 10.4324/9781315743011-9.[3] C. Rhodes, "Business Statistics," 2019.[4] J. B. Hamrick, "Analogues of mental simulation and imagination in deep learning," Current Opinion Behavioral Science, vol. 29, pp. 8-16, 2019.