University of Lincoln RIF Studentships 2014
PROJECT DETAILS
Project Title
Facilitating Individualised Collaboration with Robots (FInCoR)
Project Reference
RIF2014S-45
Project Summary
A PhD position is available in the Lincoln Centre for Autonomous Systems Research
(L-CAS), a thriving research centre based at the University of Lincoln.
L-CAS is internationally recognised for its applied autonomous systems research, in
domains such as manufacturing, agriculture, security, and care. It specialises in the
integration of perception, learning, decision-making and control capabilities in
autonomous systems such as mobile robots and smart devices.
The Centre benefits from new, modern laboratory facilities, access to state-of-the-art
robotic hardware, and offers the successful candidate a strong embedding into
existing international research projects with the potential to liaise with highly
regarded experts in the field. The candidate will be part of an international and
ambitious team, and will benefit from excellent support to produce and disseminate
original research contributions.
The PhD position is offered in the area of long-term adaptation and learning for
human-robot collaboration. The project will bring together aspects of machine
learning, AI and human-robot interaction, all with strong links to real-world application
in manufacturing and care.
The successful applicant will be an excellent student with a very good Bachelors or
Masters in Computer Science, Electronic Engineering, Mathematics or Physics who
is excited about robots and can evidence relevant coding skills
(C++/Python/Java/ROS). A background in machine learning, robotics, and/or AI is
desirable. The project start date is 1st September 2014.
The FInCoR project will investigate novel ways to facilitate individualised humanrobot collaboration through long-term adaptation on the level of joint tasks. This will
enable robots to work with human more effectively in scenario such as high value
manufacturing and assistive care.
Imagine a robot helping to assemble a car’s dashboard more effectively, knowing
that it is working with a left-handed person; or a robot assisting an elderly employee
in a car factory who is skilled in fitting a speedometer, but requires a third-hand
holding the heavy mounting frame in place. Despite significant progress in humanrobot collaboration, today’s robotic systems still lack the ability to adjust to an
individual’s needs.
FInCoR will overcome this limitation by developing online, in-situ adaptation, putting
the “human in the loop”. It will bring together flexible task representations (eg.
Markov Decision Processes), machine learning (eg. reinforcement learning),
advanced robot perception (eg. body tracking), and robot control (eg. reactive
planning) to make progress from pre-scripted tasks to individualised models. These
models account for preferences, abilities, and limitations of each individual human
through long-term adaptation. Hence, FInCoR will enable processes known from
human-human collaboration, such as two colleagues working together and learning
more about each other’s strengths, preferences, and strategies, to take place in
human-robot teams. In particular, FInCoR sets out the following objectives:
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to develop a long-term adaptation framework for task collaboration that is
governed by learning signals based on measures of performance, comfort,
and ergonomics;
to implement the adaptation framework in the de-facto standard for robot
software “ROS” to ensure effective dissemination of results and maximise
impact;
to generate high quality outputs from original research;
to explore the potential of individualised adaptation in at least two market
domains: high-value manufacturing and (assistive) care, and to validate the
framework within these domains, with input from international collaboration
partners.
Supervisory Team
1. Dr Marc Hanheide, Senior Lecturer, School of Computer Science.
http://staff.lincoln.ac.uk/mhanheide
2. Prof Tom Duckett, Professor of Computer Sciences, School of Computer
Sciences. http://staff.lincoln.ac.uk/tduckett
Informal Enquiries
For further information on this project please contact Dr Marc Hanheide by email at:
mhanheide@lincoln.ac.uk
Eligibility
All Candidates must satisfy the University’s minimum doctoral entry criteria for
studentships of an honours degree at Upper Second Class (2:1) or an appropriate
Masters degree or equivalent. A minimum IELTS (Academic) score of 7 (or
equivalent) is essential for candidates for whom English is not their first language.
Funded Studentships are open to both UK/EU students unless otherwise specified.
How to Apply
Please send a covering letter outlining your interest and proposed approach (up to 1
page A4) with an accompanying CV to mhanheide@lincoln.ac.uk by close of day on
18th April 2014.
Candidates will be notified w/c 5th May of the outcome of the process and if invited to
interview, these are anticipated to take place w/c 26h May.