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Details of Grant 

EPSRC Reference: EP/W014688/1
Title: A digital COgnitive architecture to achieve Rapid Task programming and flEXibility in manufacturing robots through human demonstrations (DIGI-CORTEX)
Principal Investigator: Oyekan, Dr J O J
Other Investigators:
Graf, Dr EW
Researcher Co-Investigators:
Project Partners:
Bosch Thermotechnology Ltd Pentaxia
Department: Automatic Control and Systems Eng
Organisation: University of Sheffield
Scheme: New Investigator Award
Starts: 01 March 2022 Ends: 31 December 2024 Value (£): 325,453
EPSRC Research Topic Classifications:
Artificial Intelligence Control Engineering
Human Communication in ICT Human-Computer Interactions
Information & Knowledge Mgmt Robotics & Autonomy
EPSRC Industrial Sector Classifications:
Manufacturing
Related Grants:
Panel History:
Panel DatePanel NameOutcome
18 Jan 2022 EPSRC ICT Prioritisation Panel January 2022 Announced
Summary on Grant Application Form
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.

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Organisation Website: http://www.shef.ac.uk