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

数字认知架构，通过人体演示实现制造机器人的快速任务编程和灵活性（DIGI-CORTEX）

• 批准号: EP/W014688/2
• 负责人: John Oyekan
• 金额: $ 36.97万
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW014688%2F2
• 关键词: digital; COgnitive; architecture; achieve; Rapid

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.

《变得更聪明》审查发现，英国在工人生产力方面落后，可以从新型工业数字工具 (IDT) 的出现中受益，例如新型智能技术、互联设备、机器人和人工智能。到 2035 年，将为英国经济增加 6300 亿英镑，并使制造业每年增长 1.5% 至 3% [1]。 [2] 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，因为大多数制造业中小企业都利用车间并为英国经济贡献高达 15% [3]，并为其配备机器人。能够快速、灵活地学习任务（类似于如何快速训练人类组装新产品线）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。该提案旨在调查认知能力。这种架构使机器人能够通过人类被动观察向机器人演示任务并将以前学到的技能应用于新任务来快速学习新技能，从而在制造车间实现任务灵活性，这开辟了令人兴奋的可能性。 ，这意味着机器人可以被教导执行各种任务，而无需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任务，从而减少对资本密集型、僵化和耗时的需求。制造设置存在差距在机器人上应用数字思维模型来构建灵活且具有创造性的机器人的文献中，这些机器人可以灵活、快速地重新分配任务以完成各种任务。然而，人们越来越认识到未来的工业机器人需要创造力，这可以实现。这是通过为他们提供心理模型来实现的[4]，该提案首次研究了一种认知架构，该架构将人类的心理模拟认知能力嵌入到一个统一的框架中，以创造性地解决制造机器人的问题和任务结构映射。达到快速的目的通过被动人类演示对工业机器人进行重新任务（任务灵活性），最先进的架构（例如 SOAR 和 ART-R）通常会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快速重新分配。给机器人分配任务，而使用机器学习范式（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任务的应用。此外，这些技术尚未通过设想成功地与数字心理模型的创建相结合。并应用于与本提案中要研究的任务类似的制造环境中的不同任务总而言之，该提案的新颖之处在于应用机器人设想的数字心理模型来支持他们对制造中遇到的问题的形态学解决方案的创造力和想象力。以及制造业以外的其他部门），以及支持将以前学到的技能应用到新的类似任务中，这将导致机器人的快速重新分配任务和任务灵活性：[1] J. Maier，“Made Smarter Review， ” 2017.[2] D. Brown、A. Swift 和 E. Smart，“数据分析和决策”，朴茨茅斯工业研究所，第 1-20 页，2019 年，doi：10.4324/9781315743011- 9.[3] C. 罗兹，《商业统计》，2019 年。[4] “深度学习中心理模拟和想象力的类似物”，《行为科学当前观点》，第 29 卷，第 8-16 页，2019 年。

项目成果

An Improved Hybrid Multi-Objective Particle Swarm Optimization to Enhance Convergence and Diversity

增强收敛性和多样性的改进混合多目标粒子群优化

• DOI: http://dx.10.1145/3583133.3596365
• 发表时间: 2023
• 作者: Islam N

A deep multi-agent reinforcement learning framework for autonomous aerial navigation to grasping points on loads

用于自主航空导航以抓取负载点的深度多智能体强化学习框架

• DOI: http://dx.10.1016/j.robot.2023.104489
• 发表时间: 2023
• 期刊: Robotics and Autonomous Systems
• 影响因子: 4.3
• 作者: Chen J

Graph-based semantic planning for adaptive human-robot-collaboration in assemble-to-order scenarios

基于图的语义规划，用于按订单组装场景中的自适应人机协作

• DOI: http://dx.10.1109/ro-man57019.2023.10309425
• 发表时间: 2023
• 作者: Ma R

A learning from demonstration framework for adaptive task and motion planning in varying package-to-order scenarios

从不同包装到订单场景中自适应任务和运动规划的演示框架中学习

• DOI: 10.1016/j.rcim.2023.102539
• 发表时间: 2024-09-13
• 期刊: Robotics Comput. Integr. Manuf.
• 作者: Ruidong Ma;Jingyu Chen;J. Oyekan
• 通讯作者: J. Oyekan