Title: Enabling robot skill acquisition and shared autonomy for undertaking complex tasks in industrial environments
Funding: Full time Home/EU fees and a stipend of £15,285 p.a.
Application deadline: 20th January 2021.
Start date: April 2021
Duration: 3 years (full time)
Location: Colchester Campus
Removing personnel from hazardous environments encountered in industrial asset and maintenance settings require Robotic Autonomous Systems (RAS) equipped with advanced cognitive abilities to cope with highly unstructured environments and complex tasks.
Although Machine Learning has shown great promise in a broad range of commercial fields, achieving similar breakthroughs within inspection and maintenance applications has proven difficult. This is mainly because RAS operating in such settings must rely on their on-board computational capacity; access to cloud infrastructure can be prohibitive either due to connectivity limitations or due to strict real-time processing requirements.
Thus, autonomy needs to be embodied within limited computing power.
The focal points of this research will be to develop compute- and data-efficient imitation learning techniques. New imitation learning techniques will need to be explored for the robot to learn both what and how to imitate. To achieve this, few-shot and zero-shot learning techniques will be considered.
There will be two core research outputs:
This project is funded by Essex University, Lloyds Register Foundation and TWI.
The studentship includes:
Lead supervisorSchool of Computer Science and Electronic Engineering, University of Essex
Industrial supervisorDirector of Essex Artificial Intelligence Innovation Centre, Research and Enterprise Office
The successful candidate would be expected to speak fluent English and meet our English Language requirements.
Note: Overseas applicants should also submit IELTS results (minimum 6.5) if applicable
You can apply for this postgraduate research opportunity online.
Please upload your CV, personal statement, and transcripts of any undergraduate or masters programmes.
Instruction to applicants
When you apply online you will be prompted to fill out several boxes in the form:
If you have any informal queries about this opportunity please email the lead supervisor, Dr Vishwanathan Mohan (lead supervisor) (email@example.com), Dr Panos Chatzakos (firstname.lastname@example.org), and Dr Anirban Chowdhury (email@example.com).