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Details of Grant 

EPSRC Reference: EP/T006951/1
Title: The Game Theory of Human-Robot Interaction
Principal Investigator: Li, Dr Y
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Articares GripAble Imperial College London
University of the West of England
Department: Sch of Engineering and Informatics
Organisation: University of Sussex
Scheme: New Investigator Award
Starts: 01 August 2020 Ends: 31 July 2022 Value (£): 236,085
EPSRC Research Topic Classifications:
Control Engineering Human-Computer Interactions
Robotics & Autonomy
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
03 Dec 2019 Engineering Prioritisation Panel Meeting 3 and 4 December 2019 Announced
Summary on Grant Application Form
Robots can offer a solution to critical societal issues such as manpower shortage in hospitals, at home and in industries due to the aging population. Instead of replacing human workers (like conventional industrial robots), new robots should co-exist and collaborate with humans. Applications of human-robot interaction can be easily found in daily lives: robot-assisted rehabilitation, where the robot helps human patients complete a certain movement and regain various functions; collaborative object manipulation, where the robot carries an object together with its human partner and shares the object load; and semi-autonomous driving, where the vehicle's controller (robot) shares the control of the vehicle with the human driver and provides assistance to them in tasks such as line following and obstacle avoidance. The interaction behaviours in these applications range from collaboration, co-operation to competition:

Application 1 (co-operation and competition): in robot-assisted rehabilitation, a robot should provide assistance to the patient when they could not complete a task by themselves; it should reduce its assistance to and even challenge the patient to promote their learning according to their recovery progress.

Application 2 (collaboration): in collaborative object manipulation, a robot and its human partner have the same target position to reach but they may have different motion plans due to their individual local sensing of the environment, so the robot should consider the human partner's motion intention when planning its own motion and possibly the human also adapts to the robot's behaviour.

Application 3 (co-operation and collaboration): in semi-autonomous driving, a robot should be able to complete a well-defined task, e.g. track a lane in normal conditions and when necessary allow the human driver to take correcting actions by shared control of steering, e.g. changing to a new road.

This project will develop a unified framework to analyse these different interaction behaviours, and more importantly, will design a robot controller to achieve natural and efficient human-robot interaction. Differential game theory, which has been proved to be powerful in modelling multi-agent systems, is a suitable choice to categorize interaction between a human and a robot. However, how it can be used to develop a robot controller that efficiently responds to its human partner needs to be investigated. Two fundamental problems will be addressed: how to continuously identify the human partner's motion planning through haptic information and how to update the robot's control strategy to ensure a desired interaction. In this project, identification techniques will be employed to estimate the partner's motion planning and control theory will be used to develop a stable and optimal robot controller. A targeted benchmark system of robot-assisted physical training will be developed to test and illustrate the power of the proposed approach in improving the training system and predicting human behaviours.

We envision that the game theory robotic controller will enable human users to interact with a robot as intuitively and efficiently as with a human, since the robot will adapt its behaviour to the human partner according to the context of the task. This project promises breakthroughs in human-robot interaction.
Key Findings
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Potential use in non-academic contexts
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Organisation Website: http://www.sussex.ac.uk