ALBERT CDT Project: Verification of Human-Robot Teamwork in Chemical Experiments


   Department of Computer Science

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  Dr Pedro Ribeiro, Dr Jihong Zhu  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

This PhD opportunity is part of the Centre of Doctoral Training in Autonomous Robotic Systems for Laboratory Experiments (Albert). It is focused on developing the science, engineering, and socio-technology that underpins building robots required for laboratory automation. Albert will contribute to the development of autonomous robots that conduct laboratory experiments that are cleaner, greener, safer, and cheaper than anything achievable with today's conventional techniques and technologies. Albert research will tackle significant socio-technical problems for science, engineering, social sciences, and the humanities. The YorRobots Executive and the Institute for Safe Autonomy will provide international leadership for this research area. The students will be provided with a rich research environment offering world-class labs and training opportunities. 

Introduction

Chemistry plays a major role in addressing society’s needs, such as preventing disease and ensuring food safety. Currently, laboratory experiments require a high degree of human intervention, limiting throughput and presenting safety risks. Increasing the automation of laboratory experimentation tasks has the potential to deliver gains in productivity and safety.

Human-robot collaboration can be beneficial to assist chemists in safely preparing and running chemical reactions. A robot can monitor progression and take action when hazards arise. Post reaction a robot can also assist with handling unknown or potentially harmful substances, for example, via joint human-robot manipulation of vials and use of rotary evaporators to remove reaction solvents. Furthermore, in complex experiments with multiple steps, a collaborative robot could make the process more efficient. The robot can prepare the next step, such as preparing vials, while the human is working on the current step, reducing the overall time taken.

However, demonstrating that robots can work safely and effectively together with humans in a lab setting is currently challenging. For example, what mathematical models are best suited to capture human-robot interactions (HRI), given the variability among humans and the adaptation that naturally occurs as humans get more familiar with a robot? Even if we consider specific scenarios, there is a need to tailor verification techniques to the domain of ALBERT. Existing techniques assume static scenarios and or consider a limited number of assumptions regarding the entities in a scenario and the dynamics of moving agents.

This research project aims to investigate the modelling and verification of human-robot collaboration in the context of laboratory experiments. The aim is to explore the use of formal verification techniques throughout the development of a robotic system to ensure safe and reliable interaction between humans and robots during an experiment.

Objectives

  • Characterise laboratory task(s) suitable for human-robot interaction (HRI).
  • Model HRI scenarios for a task involving the robot and humans.
  • Develop and tailor formal verification techniques to guarantee the safety and correctness of HRI during collaborative experiments.
  • Evaluate the performance, efficiency, and safety of the collaborative chemistry experiments compared to traditional human-led approaches.

Methodology

  1. Drawing on domain-expertise from chemists, characterise laboratory task(s), for example, joint human-robot manipulation of rotary evaporators, where HRI could be beneficial.
  2. Identify tasks from the literature where human-robot automation could be appropriate.
  3. Liaise with research and technical staff in Chemistry and conduct interviews/surveys.
  4. Draw up a list of requirements for the Human-Robot Collaborative System (HRCS)
  5. Design or adopt a Human-Robot Collaborative System suitable for the task:
  6. Consider and or design a robotic platform capable of collaborating with human chemists in a shared workspace suitable for meeting the identified requirements.
  7. Consider and or design perception and planning algorithms to enable the robot to understand and respond to human actions and intentions.
  8. Ensure seamless communication and coordination between the human and robot for efficient task allocation and execution.
  9. Formal Verification of HRI:
  10. Apply formal techniques to model and analyse the interaction between the human and robot in scenarios for human-robot teamwork in chemistry experiments.
  11. Develop formal specifications that incorporate safety constraints to ensure the correctness of the collaborative system's behaviour.
  12. Employ and tailor formal verification techniques such as model checking or theorem proving to verify the compliance of the system with the specified properties.
  13. Experimentation and Evaluation:
  14. Define a set of chemistry experiments that require close collaboration between the human and robot.
  15. Conduct experiments using the collaborative system and collect data on performance, efficiency, and safety measures.
  16. Compare the results with traditional human-led or robot-led experiments to assess the benefits and limitations of the collaborative approach.

Expected Outcomes

  • A collaborative robotic system capable of effectively working alongside human chemists in conducting chemistry experiments.
  • Tailored formal verification techniques for ensuring the safety and correctness of human-robot interaction during collaborative work.
  • Insights into the advantages and challenges of human-robot collaboration in chemistry experiments, along with the benefits of formal verification.

Training offered

The candidate will be able to attend graduate-level modules in the School of Physics Engineering Technology and the Department of Computer Science relevant to the research, such as Machine Vision and Human-Machine Interaction and Assurance & Proof. They will be able to draw on domain-expertise of the Fairlamb Group, led by Prof. Ian Fairlamb, at the Department of Chemistry, facilitated by interaction with Dr. Chris Horbaczewskyj. The university will provide training on a rich set of skills, such as, research management, leadership, academic writing, employability and public speaking. The student will also be able to take advantage of a very successful university-wide mentoring program. The student will benefit from membership of the RoboStar Centre of Excellence in Software Engineering for Robotics and training by the York Graduate Research School.

Facilities and equipment

The candidate will benefit from the facilities of the Institute for Safe Autonomy, home to over 100 experts from multiple disciplines. Specific equipment, such as a Franka Emika robot, of the Robot-Assisted Living LAboratory (RALLA) led by Dr. Jihong Zhu, will be available for the research. See full list here.


Computer Science (8) Engineering (12)

References

Othman U, Yang E. Human-Robot Collaborations in Smart Manufacturing Environments: Review and Outlook. Sensors (Basel). 2023 Jun 17;23(12):5663. doi: 10.3390/s23125663. PMID: 37420834; PMCID: PMC10304173.
Kress-Gazit, H. et al., 2021. Formalizing and guaranteeing human-robot interaction. Communications of the ACM, 64(9), pp.78-84.
Foster, S. et al., 2021. Hybrid systems verification with Isabelle/HOL: Simpler syntax, better models, faster proofs. In FM 2021, November 20–26, 2021, Proc. 24 (pp. 367-386). Springer.
Miyazawa, A., Ribeiro, P. et al., 2019. RoboChart: modelling and verification of the functional behaviour of robotic applications. Software & Systems Modeling, 18, pp.3097-3149.

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 About the Project