Centre for Doctoral Training in the area of Autonomous Robotic Systems for Laboratory Experiments (Albert CDT) Funded Projects

School of Physics, Engineering and Technology

Professor Andy Tyrell, Professor Ian Fairlamb and Dr Darren Reed

Robots are used in many applications. A number of these require grasping in one form or another. These tend to be designed for one application and “hand-designed”. A more generic approach using evolutionary methods should be able to produce grasping mechanism suitable for many applications.

The proposed project’s focus will be on autonomous execution of robotic grasping tasks, and the incorporation of evolutionary methods for on-line adaptation, both in terms of evolving the grasping mechanism(s) and evolving the robot’s behaviour and learning process. The work will involve a number of novel areas, consisting of experimental work as well as simulations: (i) Applying evolutionary methods on robotic grasping tasks on-line, using sensory data gathered from realistic tasks and then (ii) Optimising the capabilities of a robotic gripper to execute a given task in terms of hardware, intelligence, and constraint fulfilment.

The project will provide excellent opportunities for training in the areas of robotics, evolutionary computing and experimental laboratory practice.

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https://www.youtube.com/watch?v=ZFvy5jya5DU

Department of Computer Science

Professor Dimitris Kolovos and Dr Simos Gerasimou

Scientific experiments are an essential part of scientific research, as they allow scientists to test hypotheses, collect data, and validate theories. However, designing and executing experiments can be a complex and error-prone task, especially when dealing with large, complex, and dynamic systems that involve collaboration between humans and robots, such as those found in modern laboratories. Traditional approaches to experiment design and execution rely on ad-hoc solutions, such as handwritten protocols, spreadsheets, and custom software, which can be difficult to understand, maintain, and share.

Domain-Specific Languages (DSLs) [1] offer a promising alternative to these approaches, as they allow domain experts to specify experiment designs and protocols [2] using a specialised, concise, and intuitive language that is tailored to their specific needs and requirements. DSLs can be used to encode the details of an experiment, such as the materials, equipment, procedures, and measurements, in a structured and formal way that can be validated, simulated, and executed. DSLs can also provide support for experiment design and analysis, such as visualization, optimization, and verification, by leveraging domain-specific knowledge and techniques.

The project will address the following research questions:

• What types of DSLs (e.g. graphical, textual, projectional, hybrid) are most suitable to support the specification of scientific experiments that require collaboration between robots and humans (e.g. scientists, technicians) in laboratory environments?
• How can DSLs be integrated with existing experiment design and execution processes and platforms to provide a seamless and efficient experience for different stakeholders?
• How can the effectiveness and efficiency of using DSLs for scientific experiment design and execution be evaluated and compared to traditional approaches, in terms of productivity, reproducibility and collaboration?

In the context of this project the student will:

• review literature on domain-specific language engineering and on human-robot collaboration
• become familiar with state-of-the-art technologies for domain-specific language development and model management
• conduct interviews and surveys with domain experts, such as scientists and laboratory technicians, to gather requirements and feedback on the use of DSLs for experiment design and execution
• design and implement DSLs for scientific experiment design and execution, based on the identified requirements and best practices
• integrate the DSLs with existing experiment design and execution processes and platforms, and evaluate their effectiveness and efficiency through case studies, experiments, and user feedback.

Novelty

The project will produce novel methods for designing and implementing domain-specific languages and supporting tools for collaborative human-robot scientific experiments.

References

[1] Arie van Deursen, Paul Klint, and Joost Visser. 2000. Domain-specific languages: an annotated bibliography. SIGPLAN Not. 35, 6 (June 2000), 26–36. https://doi.org/10.1145/352029.352035

[2] Silva, E., Leite, A., Alves, V. et al. ExpRunA : a domain-specific approach for technology- oriented experiments. Softw Syst Model 19, 493–526 (2020). https://doi.org/10.1007/s10270-019-00749-6

[3] Ajoudani, A., Zanchettin, A.M., Ivaldi, S. et al. Progress and prospects of the human–robot collaboration. Auton Robot 42, 957–975 (2018). https://doi.org/10.1007/s10514-017-9677-2

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Department of Chemistry

Professor Ian Fairlamb, Dr Charlotte Willans, Dr Jessical Hargeaves and Dr Darren Reed

The project aims to take a critical look at our current state-of-the-art robotic and automated systems used for the chemical synthesis of pharmaceutical and agrochemical molecular targets. The project will involve organic synthesis and catalysis on a day to day basis, assessing how robotic systems can aid and assist reaction optimisation and discovery.

The principal motivation for the ALBERT mini-CDT is to develop the science, engineering, and socio-technology that underpins the building of a laboratory-based robotic system for use in applied experiments across the physical sciences. Automated laboratory experiments are revolutionizing the way that we conduct synthetic organic chemistry, from a productivity, performance and efficiency perspective. Creating a Chemistry-based eco-system that is cleaner, greener, safer, and cheaper than anything achievable by current conventional techniques and technologies, is a key driver for this research.

In this project we would like to take a critical look at our current robotic and automated systems in operation with a Chemistry research environment. How can we improve our approaches, workflows and systems for the betterment of organic synthesis and catalysis?

The following objectives are provided and we anticipate these evolving during the project:

• To explore the design of current robotic / automated systems – take a step back – to deduce whether we are already in an optimized space, or as is more likely, in a ‘what works’ space.
• To examine sustainable catalytic chemical reactions (with a focus on earth abundant metal catalysts and organocatalysts) and fully assess workflow procedures, processes and how they segway into downstream data analyses.
• To determine feasible adaptions that can be made to current robotic / automated systems, with accessibility and ease of use as key drivers.

The research employs a robotic reaction screening platform (Chemspeed), linked to instrumentation enabling data from a given reaction to be gathered (e.g. HPLC, LC-MS, IR). The ability to vary multiple reaction variables such as temperature and concentration, while gathering information (mechanistic) in tandem, provides rich data that can be harnessed, aiding optimisation of academically and industrially valuable reactions. The robotic platform allows target compounds to be synthesised in the most efficient and sustainable way, minimising side product formation while also allowing new transformations (discovery) to be fully scoped-out. The research will require synthetic chemistry experimental expertise, practical know how and a deep interest in employing a high-instrumented approach to solving challenging synthetic problems. The successful candidate will have a strong interest in data analysis and machine learning techniques although previous experience is not necessary (full training will be given).

Unlocking the true potential of any given chemical transformation is dependent on high-quality reaction data (i.e. outcomes, reproducibility and sensitivities) and its examination by multi-variate data analyses. These data can be fed back, refined, and tested in iterative cycles, enabling reaction improvement and the ultimate prediction of reaction outcomes – the ‘holy-grail’ in chemical synthesis. We expect novelty in detailed reaction understanding and/or new reaction discovery.

The project will be led by Ian Fairlamb and supported by colleagues (co-supervisors) from Chemistry (Charlotte Willans), Mathematics (Jessica Hargreaves) and Sociology (Darren Reed). Each provide unique, complementary expertise and skills that are key to delivering this ambitious project. From rigorous mechanistic understanding of pharma-relevant catalysis, automated synthesis and rich data analysis, medicinal chemistry-related methodology, assessment of reaction outcome data through to statistical analysis. Moreover, the sociological context of how the robotic systems are interacting with chemists will be examined – are we doing this in the right way and are there any interactions that we are missing out on?

There will be cohort-based training for the ALBERT mini-CDT, where other students working on related projects (across a range of York-based Departments) will meet to exchange ideas, solve problems and discuss alternative ways to improving automated laboratory experiments going forwards.

Our current in-lab Chemspeed ISYNTH robotic system enables automated reaction screening and monitoring (full-training will be provided by a dedicated highly trained research technician). We will further provide full training in Principal Component Analysis and other data analysis tools. The team have supervised well over 40 PhD students to completion. High quality, supportive and inclusive mentoring will be given, and the student will be involved in all of our research activities.

Given the increasing number of job opportunities in Industry in data science and automated reaction optimisation, we believe that the graduated PhD candidate will be placed well. Moreover, they will have the chance to grow their future careers in this space.

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https://www.youtube.com/watch?v=JXhjmvVm1_8

Department of Chemistry

Dr Charlotte Willans, Professor Ian Fairlamb and Dr Laurence Wilson

This project will explore the use of robotics to reduce and ultimately eliminate the toxic effects of nickel and other metals when used in processes relevant to the pharmaceutical and agrochemical sectors.

Organometallic catalysis is one of the most vibrant and essential areas worldwide in scientific research, with impact in a broad range of industrially relevant fields such as
pharmaceuticals, agrochemicals and materials. Many metal-catalysed reactions rely on the use of precious metals such as palladium, iridium and rhodium; the high cost of these metals and risk of dwindling supply render these processes unsustainable. Attention over the last decade has turned towards the development of more abundant and cheaper base-metals. Major challenges in this field are a lack of understanding and low predictability, thus significantly higher loadings of catalyst are used when compared to precious metals. Despite being more abundant and cheaper, many of the base metals pose significant toxic risks, both when handling the precursors prior to the reaction and in their disposal, particularly as high loadings are used. Nickel, which has been developed as a highly attractive alternative for many metal catalysed processes, is highly toxic. This project will explore the use of robotics to reduce and ultimately eliminate the toxic effects of nickel when used in processes relevant to the pharmaceutical and agrochemical sectors. The technological development will also be relevant to other types of metal catalysts and processes.

The following objectives are proposed and will evolve during the project:

• To electrochemically generate nickel species from a nickel plate, and screen in catalytic reactions of relevance to the chemicals industry
• To examine electrochemical conditions under which the nickel metal can be efficiently removed from the stream, without forfeiting the catalytic reaction product
• To develop a closed-loop system in which the nickel species is electrochemically generated, used directly in a catalytic reaction, with subsequent electrochemical removal and reuse.

The research will make use of electrochemical flow reactors that have been developed in the group. These will be coupled with catalytic flow reactors, hence the generated nickel species dispensed directly into the catalytic reaction. Development of online analytical tools (e.g. HPLC, UV, MS) will enable reactions to be monitored in real-time. As the electrochemical recovery part of the project evolves, reactor technology will require further development to combine into a closed loop system. The proposed work will require a researcher with synthetic chemistry experience, an understanding of catalysis and analytical methods used for monitoring reactions, and an enthusiasm to learn new techniques such as electrochemical synthesis, flow chemistry and use of software for automation and data handling.

Whilst a lot of research in base-metal catalysis focuses on performance and development of complexes for a particular reaction, little attention is given to how this might be translated to large-scale use. Although base-metals are more abundant and cheaper than precious metals, safety and sustainability must still be considered. This project addresses both of these challenges and works towards a more sustainable and cleaner chemicals industry.

The project will be led by Charlotte Willans and co-supervised by colleagues in Chemistry (Ian Fairlamb) and Physics (Laurence Wilson). Each bring complementary expertise to the project which are key to ensuring success. The student will develop their skills in synthesis and catalysis, in addition to analytical skills. Furthermore, they will learn new skills in electrochemical synthesis, flow chemistry and using software to control instruments, monitor reactions and handle data. The student will be provided with additional training in good laboratory practice, presentation and writing skills, literature searching and critique, and have the opportunity to use these throughout their PhD at group and supervisory meetings, CDT meetings and at national and international conferences. All this will provide the student with an excellent background for a career in a broad range of fields.

There will be cohort-based training for the ALBERT mini-CDT, where other students working on related projects (across a range of York-based Departments) will meet to exchange ideas, solve problems and discuss alternative ways to improving automated laboratory experiments going forwards.

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https://www.youtube.com/watch?v=WYIV7SvRRNU

Department of Psychology

Dr Cade McCall, Professor Ana Cavalcanti and Professor Jason Lynam

Human decision-making is critical for the safety and efficacy of supervised autonomous systems. As such, human behaviour should be accounted for in their design and verification. But modelling human decision-making is not trivial. Humans deploy a wide variety of cognitive processes to make decisions in the complex, dynamic and unpredictable environments in which we live and work. We use prior knowledge, attend to ongoing changes in context, reason about the likely outcomes of possible choices, and weigh their costs and benefits. But key questions arise with the introduction of autonomous systems to living and working environments. Does sharing a task with an autonomous system shape our approach to dealing with uncertainty, our attention to key contextual factors, our willingness to take risks, or our ability to learn through trial and error? Addressing questions such as these will help us anticipate how autonomous systems shape our cognition in situations where human input is critical but potentially fallible. Moreover, answering them will help us incorporate accurate models of human behaviour when designing safe and effective autonomous systems. The proposed project will explore these ideas with specific reference to autonomous systems designed for use in chemistry labs. It will examine how such systems can support the decision-making involved in a laboratory’s data acquisition and analysis, while identifying more general principles for optimizing human-robot interaction.

1. Identify key parameters of human decision-making that are likely to be affected by collaborative work with autonomous systems in laboratory environments. These
   parameters will be drawn from the extant decision-making literature, making use of recent advances in cognitive modelling of dynamic decision-making.
2. Develop simulations in which human users complete dynamic decision-making tasks with, and without, the collaboration of an autonomous system. These simulations
   will be based on existing human decision-making research paradigms that lend themselves to collaborative (i.e., human and autonomous system) decision-making. They will simulate chemistry laboratory environments in which users are tasked with data acquisition and analysis.
3. Gather and analyse human behavioural data using the paradigms developed in Objective 2. Analyses will employ contemporary approaches to cognitive modelling (e.g., reinforcement learning, instance-based learning, etc.).
4. Provide recommendations for key factors to consider when modelling “humans in the loop” in designs for laboratory-based autonomous systems.

This project will take an empirical, quantitative approach to addressing its questions. It will rely upon human behavioural data collected in laboratory-based studies. These experiments will use immersive or desktop simulations in which individuals complete a series of decisions in a dynamic virtual environment. Data analyses will use cognitive modelling approaches to extract relevant parameters from the data and multilevel models to evaluate differences between key experimental conditions.

This research will apply contemporary approaches to measuring and modelling human decision-making to the unique contexts of human collaborations with laboratory-based autonomous systems.

• Alongside PhD research, the successful candidate will have access to training opportunities to enhance research skills and broaden their knowledge of psychology.
  First year modules include modules on research design and statistics as well as practical skills in experimental, cognitive, and social psychology. Weekly research seminars in the Department of Psychology include a ‘meet the speaker' event where one can talk informally with high-profile academics and other external speakers.
• The successful candidate will also be part of a cohort of our Centre for Doctoral Training on Autonomous Robotic Systems for Laboratory Experiments. This will involve
  regular meetings with students from other disciplines working on similar topics.
• The successful candidate will further benefit from the general training provided by the Departments of Computer Science and Chemistry. This will cover topics such as
  security, research management and leadership, collaborations, employability, public engagement and communication.

References

Gonzalez, C. (2017). 13 Decision-Making: A Cognitive Science Perspective. The Oxford handbook of cognitive science, 249.

McCall, C., Schofield, G., Halgarth, D., Blyth, G., Laycock, A., & Palombo, D. J. (2022). The underwood project: A virtual environment for eliciting ambiguous threat. Behavior research methods, 1-16.

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https://www.youtube.com/watch?v=GvwbmRFWQww

Department of Sociology

Dr Darren Reed, Professor Ian Fairlamb, Professor Jim Woodcock

The Albert Centre for Doctoral Training is concerned with the design of interactive robotics for the Chemistry Laboratory. As part of these efforts, we are looking for a talented social scientist with skills in ethnography, to undertake a close observation of current laboratory practice. This includes the use of laboratory technology and the human practices of interaction and sense-making.

You will have a fascination for human interaction and the ability to discern the manner in which the laboratory space is meaningfully constructed. In addition, you will be interested in the way that embodied human interaction is coordinated and afforded by the scientific technologies and the laboratory spaces.

You will have expertise in workplace studies and an understanding of ethnomethodology and other forms of empirical sociology. You will have a working knowledge of human-computer interaction design and an ability to convey your research in a timely and constructive manner.

The principal motivation for the ALBERT mini-CDT is to develop the science, engineering and socio-technology that underpins the building of a laboratory-based robotic system for use in applied experiments across the physical sciences. Creating a Chemistry-based eco-system that is cleaner, greener, safer, and cheaper than anything achievable by current conventional techniques and technologies, is a key driver for this research.

This research will study the everyday practices of those in the chemistry lab. It will:

• identify the mundane relationships between human practices, laboratory space, and technical devices
• focus on the use and integration of assistive and automated devices in the production process
• develop a detailed account of the socio-technical relationships in terms of safety regulations, material logistics, and organisational relationships
• provide clear design requirements for other members of the Albert CDT.

The social researcher will undertake a focused ethnography of a chemistry laboratory. This will be oriented to identifying and detailing the mundane interactional practices between humans and the various technical devices and spaces within the lab. This will rest on a grounding in ethnomethodology and forms of interactional analysis such as embodied conversation analysis.

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Department of Computer Science

Professor Ana Cavalcanti and Professor Ian Fairlamb

Advances in mechanics, electronics, computer vision, and artificial intelligence have recently enabled the development of exciting new robotic systems, varying from driverless vehicles to home assistants and industrial robots. In this project, we consider robots for use in Chemistry Laboratories, in particular with widely used equipment: fume cupboards.

Fume cupboards are extensively used in a range of experiments. Use of these cupboards can cause physical strain, and so the amount of time that a human can operate a cupboard. To allow a robot to operate a fume cupboard, however, we need to trust that robot. Potential consequences if they fail in their operation can range from wasted time and materials, to more serious incidents involving dangerous chemicals.

Technology for development and verification of trustworthy applications is the topic of much research. Software engineering techniques that provide appropriate and specific support for robot engineers are few and far between.

In this project, we will apply state of the art technology for modelling, simulation, and testing for analysis and verification of robots that can be used with fume cupboards. We will consider, first, existing technology to evaluate the limits of what can be achieved with existing robots and existing Software Engineering techniques. Second, we will consider advanced designs that support adaptation to deal with a variety of experiments and unexpected changes to the cupboard.

Contributions of the work will span from improvements to automation of Chemistry Laboratory to novel techniques for modelling, design, and verification of adaptive software for robotics. Our challenge is to enable and promote development approaches, where practitioners deal with models using accessible domain-specific languages, validation and verification is via modern simulation, testing, and proof techniques. Any problems are solved by revisiting the models, not tinkering with low-level code. There is scope for foundational and applied contributions.

The successful candidate will benefit from the general training provided by the Departments of Computer Science and Chemistry. This will cover topics such as security, research management and leadership, collaborations, employability, public engagement and communication. Moreover, the candidate will benefit from inclusion in a cohort for our Centre for Doctoral Training on Autonomous Robotic Systems for Laboratory Experiments. This will involve regular meetings with students working on similar topics and the same case studies.

References

A. L. C. Cavalcanti, W. Barnett, J. Baxter, G. Carvalho, M. C. Filho, A. Miyazawa, P. Ribeiro, and A. C. A. Sampaio. RoboStar Technology: A Roboticist's Toolbox for Combined Proof, Simulation, and Testing, pages 249--293. Springer International Publishing, 2021. [ bib | DOI | .pdf ]

A. L. C. Cavalcanti. RoboStar Modelling Stack: Tackling the Reality Gap. In 1st International Workshop on Verification of Autonomous & Robotic Systems, VARS 2021. Association for Computing Machinery, 2021. [ bib | DOI ]

A comprehensive list of publications is provided in the RoboStar site.

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https://www.youtube.com/watch?v=beLGNGHl7o0

Department of Computer Science

Professor Radu Calinescu and Professor Helen Sneddon

Many compounds used in industry and research labs are obtained through complex multi- stage chemical processes whose design requires the selection of suitable reactants and chemical reaction parameters such as reactant concentrations, temperature and pressure. This selection involves the exploration of very large design spaces, and can have significant safety and sustainability implications. The mathematical modelling of such processes as chemical reaction networks enables the analysis and comparison of their design options.

The project will tackle the challenging problem of synthesising chemical reaction networks (CRNs) for building user-specified chemical compounds safely and with a low carbon footprint. The project will model CRNs as continuous-time Markov chains and/or ordinary differential equations, assemble a repository of patterns of CRN fragments annotated with safety concerns and carbon-footprint levels, and use metaheuristics to instantiate and combine these patterns into end-to-end CRNs guaranteed to produce the required chemical compounds safely and with a low carbon footprint.

In a first stage, the project will model and analyse chemical processes proposed by industrial partners, identify opportunities for calibrating these processes for safety and sustainability, and validate the calibrated processes with these partners. Next, the findings from the first stage will be generalised into a methodology for the synthesis of green and safe chemical reaction networks, and the methodology will be validated through application to additional chemical processes.

While chemical reaction networks have been widely used for the design, analysis and calibration of complex chemical processes, their potential to support the synthesis of
chemical processes that are both safe and sustainable remains underexplored.

The PhD student will attend relevant graduate-level modules in the Departments of Chemistry and Computer Science, undertake visits at industrial partners, and participate in the wide range of training activities organised by our York Graduate Research School.

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York Law School

Professor T.T Arvind and Dr Darren Reed and Professor Ian Fairlamb

The Albert Centre for Doctoral Training is concerned with the design of interactive robotics for the Chemistry Laboratory. As part of these efforts, we are looking for a talented lawyer with a sound background in regulation and empirical socio-legal studies to carry out research into the relationship between rules, practices, and regulation, and into how abstract policies are translated into practical processes and practices in the context of scientific laboratories. The work you will carry out will include understanding the complex relationship between the regulatory world of policy and the experiential world of practice.

You will have a fascination for human interaction with law and regulation and the personal and organisational practices through which people working in scientific contexts navigate the mesh of rules applicable to them within the laboratory space; and the processes through which they construct the relationship between rule, conduct, and space. You will also be interested in the way that embodied human interaction is shaped by their understanding of the scientific mission they see themselves as pursuing, their views as to the world of law, and the organisational cultures in which they are embedded.

You will have expertise in regulation studies and an understanding of empirical socio-legal studies. You will have a working knowledge of law and technology and/or science and technology studies, and an ability to carry out and disseminate research in a timely and constructive manner.

The principal motivation for the ALBERT mini-CDT is to develop the science, engineering, and socio-technology that underpins the building of a laboratory-based robotic system for use in applied experiments across the physical sciences. Creating a Chemistry-based eco-system that is cleaner, greener, safer, and cheaper than anything achievable by current conventional techniques and technologies, is a key driver for this research.

This research will study the multifaceted relationship between the regulatory world of policy and the experiential world of practice in the specific context of the chemistry laboratory. It will:

• study the manner in which the regulations driving the chemistry lab are instantiated at a practical and interpersonal level within, and through, everyday behaviour in the laboratory
• identify the role and impact of relational and organisational hierarchies of control, the emergence of organizational and disciplinary cultures through the communication of rules and practices via instruction and implicitly through practice, the material and textual instantiations of rules and procedures, and the establishing of habitual behaviours and practices
• develop a detailed understanding of the manner in which these behaviours and practices interact with regulatory goals, procedures, and structures to provoke questions of adherence and responsibility
• develop a detailed account of the practical instantiation of regulation in practice as instituted, socio-technical process
• draw on your findings to provide clear design requirements for other members of the Albert CDT.

The legal researcher will undertake a focused study of the development of compliance-oriented and compliance-informed processes, procedures, practices and cultures in
chemistry laboratory. The purpose of the study will be to identify the manner in which regulatory goals and policies are translated into concrete processes and practices, and the cultures of responsibility and adherence to which they give rise. Your work will be informed by a rich and nuanced engagement with regulatory theory, which you will combine with empirical socio-legally informed analysis of a laboratory environment.

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