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  Adaptive Robotic Behaviours in dynamic and outdoor settings.

   School of Computing, Engineering & the Built Environment

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  Dr Leni Le Goff  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

In controlled settings such as factories, robots are able to achieve many tasks efficiently and accurately. However, it is still a challenge to enable robots to operate in unstructured, dynamic and outdoor environments. In such settings, changes can occur that can render the skills and knowledge of the robot ineffective. Robots must therefore be able to adapt previously learned behaviours to new tasks and settings. The approach proposed to be investigated is in two steps. First, existing resource intensive algorithms1 will be applied to learn robust behaviours and perceptual representation for the robot to tackle complex tasks and environments. In this first step the environments will be static. Then, light weight algorithms2,3, e.i. with fast convergence, will be explored to adapt quickly these learned behaviours and representations to face dynamic environments. The ultimate goal of this project is to enable robots to achieve complex task in outdoor settings4 where the conditions can change suddenly or progressively. Using mobile legged robot such as dog or hexapod robot, the Ph.D. work will focus first on testing the viability of the methods in simulation before eventually testing them on a real robotic platform.

Academic qualifications

A first degree (at least a 2.1) ideally in computer science and robotic with a good fundamental knowledge of C++ and Python programming, optiomisation and machine learning theory and techniques.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University's policy are available online.

Essential attributes:

  • Experience of fundamental C++ and Python programming
  • Competent in optimisation and machine learning theory and techniques
  • Knowledge of robotics mathematics: solid mechanics, 3D geometry, control theory etc.
  • Good written and oral communication skills
  • Strong motivation, with evidence of independent research skills relevant to the project
  • Good time management

Desirable attributes:

  • Basic knowledge in computer vision, operating systems and network.

For enquiries about the content of the project, please email Dr Leni K.Le Goff [Email Address Removed]

For information about how to apply, please visit our website

To apply, please select the link for the PhD Computing FT application form

Computer Science (8) Engineering (12) Mathematics (25)

Funding Notes

Funded PhD Project (Worldwide)


1. Cully, A., Clune, J., Tarapore, D., & Mouret, J. B. (2015). Robots that can adapt like animals. Nature, 521(7553), 503-507.
2. Le Goff, L. K., Yaakoubi, O., Coninx, A., & Doncieux, S. (2022). Building an affordances map with interactive perception. Frontiers in Neurorobotics, 68.
3. Chatzilygeroudis, K., Vassiliades, V., Stulp, F., Calinon, S., & Mouret, J. B. (2019). A survey on policy search algorithms for learning robot controllers in a handful of trials. IEEE Transactions on Robotics, 36(2), 328-347.
4. Miki, T., Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., & Hutter, M. (2022). Learning robust perceptive locomotion for quadrupedal robots in the wild. Science Robotics, 7(62), eabk2822.
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