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

Made Smarter评估发现，英国在工人生产率方面落后，可能会受益于新的工业数字工具(IDT)的出现，如新型智能技术、互联设备、机器人和人工智能。据估计，到2035年，IDT可以为英国经济贡献额外的6300亿GB，并使制造业每年增长1.5%至3%[1]。例如，大量定制和个性化商品的需求很高[2]。为了满足这一需求，制造系统需要具有高度的灵活性、适应性和高度自动化。由于大多数制造业中小企业利用工作场所，对英国经济的贡献率高达15%[3]，为它们配备能够快速灵活地学习一项任务的机器人(类似于如何快速培训人类组装新的生产线)，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。这项提议旨在研究认知架构，使机器人能够通过被动观察人类向机器人演示任务并将先前学到的技能应用于新的任务场景，从而在制造车间实现任务灵活性，从而使机器人能够快速学习新技能。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会做各种任务，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执行各种任务，从而减少对资本密集型、僵化和耗时的制造设施的需求。在将数字心理模型应用于机器人以建立灵活和创造性的机器人方面，文献中存在空白，这些机器人可以灵活而快速地重新执行各种任务。然而，人们越来越意识到，未来的工业机器人需要创造力，这可以通过为它们提供心理模型来实现[4]。该方案首次研究了一种认知体系结构，该体系结构将人类在制造机器人上进行创造性问题解决的心理模拟认知能力和任务结构映射嵌入到统一框架中，目的是通过被动的人类演示来实现工业机器人的快速重新任务(任务灵活性)。最先进的体系结构(如SOAR和ART-R)通常利用先前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上的快速重新任务，而使用机器学习范例(例如，深度神经网络或强化学习)的技术需要大量数据并导致特定于任务的应用。此外，这些技术还没有成功地与通过想象创建数字心理模型相结合，并应用于制造环境中的各种任务，类似于本提案中将要研究的任务。总而言之，这项提议的新颖性在于应用了机器人设想的数字心理模型，以支持他们创造性地和想象力地从形态上解决制造业(和制造业以外的其他部门)遇到的问题，并支持将以前学到的技能应用于新的类似任务。这将导致机器人快速重新执行任务和任务灵活性。参考文献：J.Maier，“Made Smarter Review，”2017.[2]D.Brown，A.Swift，and E.Smart，“Data Analytics and Decision Making，Inst.印地安人。研究对象：大学朴茨茅斯，第1-20页，2019年，DOI：10.4324/9781315743011-9。[3]C.罗兹，《商业统计》，2019年。[4]J.B.哈姆里克，《深度学习中心理模拟和想象的类比》，《当前观点行为科学》，第29卷，第8-16页，2019年。

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.