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  Deep learning of deformable object manipulation on manufacturing robots

   Department of Computer Science

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  Dr John Oyekan  Applications accepted all year round  Self-Funded PhD Students Only

About the Project


Current robots in manufacturing settings are not able to adapt to variations introduced by collaborating humans or other factors in the environment. This is especially true when the robot has to manipulate objects that are deformable and flexible.

However, the real time cognitive ability of humans and animals provide us a lot of inspiration for creating resilient and robust artificial intelligent systems that optimise in real time to problems and variations in their environment especially when dealing with deformable objects. A potential research question is: “How can the skills learnt in one task be transferred across to another task involving deformable objects?” The successful candidate will work with a team of researchers and industrial partners on the project DigiCORTEX (


One of the paradigms that has been created by the machine learning community is the Reinforcement Learning paradigm. In reinforcement learning, the focus is on applying rewards from the environment to learn as well as finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) to find solutions to problems. The paradigm relies on a prior well-defined environment and results in a control policy that is best suited for the environment that it was designed for. It starts suffering when there are variations such as the ones introduced by deformable objects in the environment.

Aim and objectives

Informed by cognitive psychology literature and supported by neuro-evolution [1], meta-learning, neural architecture search methodologies [2][3] as well as recent work in reinforcement learning for deformable object manipulations [4][5], this PhD topic aims to develop new real time cognitive approaches and computation to create robots that can flexibly learn new ways of manipulating deformable objects and transfer learnt tasks to new settings.


  1. Investigate various frameworks that support how humans interact with deformable objects.  
  2. Develop a framework that embeds meta-learning and neural architecture search methodologies
  3. Develop a robotics simulation environment for various use cases that deal with deformable objects. The successful student would work with our industrial partners to create various use cases that suit their industrial needs. You will have an opportunity to collaborate with our industrial partners on various use cases in sectors such as Manufacturing, Space, Agriculture etc.
  4. Integrate objectives 2 and 3 into the development of new computational intelligent algorithms.
  5. Validate algorithms on 2 collaborative robot platforms. You will have access to various robot platforms including the Franka arm, Sawyer, UR5 and UR15.   

This work will be carried out in the Department of Computer Science (top 10 in UK for research quality), YorRobots ( and at the University of York’s £15m institute for safe autonomy ( The Institute for Safe Autonomy is UK’s first research centre dedicated to the design, development, safety and communications for robotics and connected autonomous systems. The Institute provides a world-leading ecosystem for research and innovation, education, public engagement and commercial realisation. You will also be expected to collaborate with our industrial partners on various use cases from time to time.

The University of York is part of the research-intensive Russell Group of Universities in the United Kingdom which inject nearly £87 billion into the national economy every year. We believe that people and ideas are the key to meeting global challenges. Through world-class research and education we are helping to create a dynamic economy, stronger communities and a better future for the world. We maintain the very best research, an outstanding teaching and learning experience and unrivalled links with local and national business and the public sector.

Russell Group universities have huge social, economic and cultural impacts locally, across the UK and around the globe. The Russell Group of Universities produce more than two-thirds of the world-leading research produced in UK universities and support more than 260,000 jobs across the country. 32% of students are of non-UK nationality, attracted to our universities by the quality, relevance and reputation of research. Russell Group members have a strong role and influence within their regional and local communities, collaborate with businesses on joint research projects and supply highly-qualified and highly-motivated graduates to the local workforce. 

Computer Science (8)


[1] Chalumeau, F., Boige, R., Lim, B., Macé, V., Allard, M., Flajolet, A., Cully, A. and Pierrot, T., 2022. Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery. arXiv preprint arXiv:2210.03516.
[2] Hospedales, T., Antoniou, A., Micaelli, P. and Storkey, A., 2021. Meta-learning in neural networks: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(9), pp.5149-5169.
[3] Finn, C., Abbeel, P. and Levine, S., 2017, July. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning (pp. 1126-1135). PMLR.
[4] Nvidia Developer.NVIDIA’s physics simulation environment for reinforcement learning research.
[5] Lin SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation. and

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 About the Project