Elle Miller

Research project title: 
Deep Reinforcement Learning for Safe and Compliant Human-Robot Interaction
Research project: 

Robots possess significant potential to enhance the quality of life for individuals with disabilities, support healthcare professionals, and provide care to an ageing population. To help with tasks such as drinking, dressing, and personal hygiene, robots will need to perform very intricate behaviours while in direct physical contact with humans. Deep Reinforcement Learning (DRL) has emerged as a promising avenue to acquire diverse complex behaviors safely through simulation. However, an open challenge lies in transferring these learned policies to real-world scenarios while ensuring safety. In my research I aim to investigate the potential of DRL to learn safe and compliant assistive behaviours for physical human-robot interaction.


About me: 

I am a PhD student in both the [Statistical Learning and Motor Control Group](https://web.inf.ed.ac.uk/slmc) and [Autonomous Agents Research Group](https://agents.inf.ed.ac.uk/), under the supervision of Sethu Vijayakumar and Stefano Albrecht.

Previously, I was a Research Engineer at DLR’s Institute for Mechatronics and Robotics, working on an inspiring assistive robotics system called [EDAN](https://www.dlr.de/rm/en/desktopdefault.aspx/tabid-11670#gallery/28208). Prior to that, I was a Visiting Student Researcher at NASA’s Jet Propulsion Laboratory working on robotic autonomy for the DARPA [RACER Challenge](https://costar.jpl.nasa.gov/racer/). Along the way, I also helped create a unique mobile humanoid robot called [EVA](https://youtu.be/nMkcBbofDY0).

I graduated with First Class Honours in a Bachelor of (Mechatronic (Space)) Engineering and a Bachelor of Advanced Science (Physics) from the University of Sydney in 2023. As an undergraduate, I undertook several internships in parallel with my studies, working with the Max Planck Institute of Astronomy, University of Cambridge, Cochlear, Australian National University, and Saber Astronautics.

Student type: 
Current student