First Joint Workshop of Robotics and Mathematics

Wednesday 12 June 2019 (10:00-17:30)

Dr Sergey Sergeev on or +44 (0) 121 414 6592

This is a multidisciplinary workshop whose purpose is to bring together Robotics and Applied Mathematics at the University of Birmingham. It will open in the morning, with the talks from the Robotics Group led by Professor Rustam Stolkin.

In the afternoon, the many facets of our Applied Mathematics research are going to be presented. As usual, most of the discussion will take place at lunch and coffee breaks, and the dinner will also follow. 


Vaughan Jeffreys Lecture Theatre, School of Education (R19 on campus map).



  • Irum Mehboob ( School of Computer Science)
  • Maxime Adjigble (School of Computer Science)
  • Claudio Zito (School of Computer Science)



10:00 - 10:30 Maxime Adjigble

  • Title: Human Robot Interaction- Assisted telemanipulation
    Abstract: Robotic telemanipulation plays a significant role in accomplishing  a  variety  of  challenging  tasks  in  various  fields. It  enhances  human  manipulation  capabilities  in  remote  and safety-critical  environments,  where  direct  human  interaction with the scene objects is not considered feasible. For instance, telemanipulation  systems  are  used  in  the  medical  field  to execute  complex  surgical  procedures  and  in  nuclear settings  they help  in  carrying  out  complex  decommissioning tasks. Despite all the obvious advantages of telemanipulation, the implementation and wide adoption of these technologies is still in a primitive stage due to the nontriviality of using the existing solutions. We will discuss new methods that allows to fully take advantages of telemanipulation systems.
    The slides of my talk are available at: Human Robot Interaction- Assisted telemanipulation (PDF)

10:30-11:00 Irum Mehboob

  • Title: What is the “best” optimisation method in deep learning?
    Abstract: Deep learning , as a very important part of machine learning, has given advancement in computer vision tasks. It helps in accurate object detection, localisation, classification , robot grasping and manipulation tasks.  Deep learning is based on  mathematical tools such as back propagation, loss function and optimisation techniques.  There are existing optimisation techniques which are used in deep learning to minimise the loss . This means to predict the parameter values which match the ground truth values of the data. Some of them are for small datasets and some are for large datasets like Adam. My goal is to identify new and efficient optimisation technique which can be used both for small datasets and as well as large datasets but with more emphasis on large datasets, which become more and more prominent these days.

11:00-12:00 Claudio Zito

Title: Transferable Learning for Robotic Manipulation Tasks
Abstract: As robot make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable circumstances. It is clear that the ability of predicting how their actions affect the environment is a key skill that they need to possess to operate in safety and gain our trust in such environments. Humans perform skilful manipulation tasks from an early age on, and are able to transfer behaviours learned on one object to objects of novel sizes, shapes, and physical properties. For robots, achieving those goals is challenging. For one thing, this complexity arises from the fact that physical and geometrical properties of the environment are usually uncertain, or even unknown, but play a significant role for manipulation tasks.
I will present a set of models that can learn transferable skills for manipulation tasks. The key insight is to represent these tasks in terms of local contacts and how these contacts change over time. These models allow us to generalise within and across object categories by exploiting local similarities between objects of different shapes. I will present the mathematical formulation to compute and use such models, focusing on robotic task such as grasping and pushing of novel objects.
You can read more about my research on my personal Webpage:

12:00 Lunch (Staff house)

13:00-13:30 Coffee and Tea  (Vaughan Jeffreys)

13:30-14:00 Michal Kocvara

  • Title:Linear and Nonlinear Semidefinite Optimization by PENNON
    Abstract: Michal will give a brief overview of the software package PENNON for large scale linear and for general nonlinear semidefinite optimization. To answer the question why we need software for nonlinear SDP, he will discuss the Static Output Feedback (SOF) problem formulated first as a problem with bilinear matrix inequalities and then as a problem with polynomial matrix inequalities.
    Here are the slides of my talk: Linear and Nonlinear Semidefinite Optimization by PENNON (PDF)

14:00-14:30 Hong Duong

  • Title: A class of generalized potential games and applications
    Abstract: In the seminal work [1], Monderer and Shapley introduced a fundamental concept of potential games. They are a class of games where the incentive of all players to change their strategy can be expressed via a single global function called the potential function. Using the potential function, the existence of pure-strategy Nash equilibria and the convergence to these equilibria have been shown. Potential games have been studied intensively in theoretical research and have been found in a cornucopia of practical applications in economics and other disciplines such as artificial intelligence and computer vision, theoretical computer science, computational social science and sociology and wireless networks. Recently, there has been a growing interest on game theory in machine learning community. However, the definition of potential games is rather restricted because it requires that the simultaneous gradient of the payoff functions is equal to the gradient of the potential function. In this talk, we are interested in multi-player differentiable games. We establish a connection between weighted potential games and symmetrizable matrices and introduce a notion of generalised potential games that is inspired by a newly developed theory on (generalised) gradient flows in the field of partial differential equations. We also discuss some applications and recent interest in game theory in machine learning.

This talk is based on a joint work with H. Dang (Google, Zurich), B. Tang (Graz, Austria) and H. Tran (Esmart Systems, Norway). [1] D. Monderer and L. S. Shapley. Potential games. Games and economic behavior, 14(1):124-143, 1996.

Here are the slides of my talk: A class of generalized potential games and applications (PDF)

14:30-15:00 Natalia Petrovskaya

  • Title: Dealing with sparse and noisy data in problems of spatial ecology
    Abstract: In the modern world of big data there are still a plenty of ecological applications where spatial data available for analysis are extremely sparse because of financial, labour, and other reasons. In addition, data obtained from field measurements are often noisy. In my talk I will consider several examples taken from spatial ecology where sparse and/or noisy data are used for reconstruction of spatial patterns and evaluation of functionals from spatial data. Among the other results, it will be demonstrated in the talk how using sparse data converts a deterministic problem into the probabilistic one.
    Here are the slides of my talk: Dealing with sparse and noisy data in problems of spatial ecology (PDF)

15:00-15:30 Coffee and Tea (Vaughan Jeffreys)

15:30-16:00 Tom Montenegro-Johnson

  • Title: Microscale swimming robots: propulsion and control
    Abstract: Tom will discuss some recent theoretical and experimental advances in the field of artificial swimming particles, focusing on mechanisms of propulsion, fine control, and fabrication, concluding with some novel applications and possible future directions

16:00-16:30 Panayiota Katsamba

  • Title: Application-driven design of microbots
    Abstract: Bio-inspired microswimmers that mimic bacterial locomotion achieve propulsion at the microscale level using magnetically actuated, rotating helical filaments. A promising application of these artificial microswimmers is in non-invasive medicine, for drug delivery to tumours or microsurgery. Panayiota will discuss how mathematical modelling can address two crucial features in the design of microswimmers. First, the ability to selectively control large ensembles and second, the adaptivity to move through complex conduit geometries, such as the constrictions and curves of the tortuous tumour microvasculature. 

16:30-17:00 Sam Johnson

  • Title: How does it feel to be a neural network?
    Abstract: Neural networks underpin the most celebrated recent breakthroughs in artificial intelligence. This is a spectacular example of biomimicry: the concept of a network of dynamical elements whose interaction strengths could be tuned to encode information was discovered by observing the brain. I will discuss some recent results on the relationship between digraph topology and dynamics, once again inspired by living systems, and suggest potential synergies with artificial intelligence research.
    Here are the slides of my talk: How does it feel to be a neural network? (PDF)

17:00-17:30 Coffee and Tea (Vaughan Jeffreys)

17:30+  Dinner

We plan to go for an early dinner. The venue will be announced later, shortly before the workshop. Please inform Sergey whether you are going to join him and the speakers for dinner. 


For more information, please contact Dr Sergey Sergeev on or +44 (0) 121 414 6592

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