View Proposal

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Proposer

Marta Romeo

Title

Trust modelling for human-robot interaction

Goal

Description

Trust is essential for successful human-human interactions and plays a major role in human-robot interactions, influencing the human’s willingness to accept information from a robot and to cooperate with it. Modelling trust could therefore be a vehicle to build more intelligent social robots. For this reason, much work has been done in trying to identify the factors defining trust and a computational model that could encapsulate the concept. Bayesian models and reinforcement learning have shown promises in this respect. Although trust evolves as the interaction evolves, in many works time is not fully taken into consideration. The objective of this project will be to dive into the literature on trust modelling to develop and test (through simulations) a cognitive architecture on the evolution of trust in human-robot interaction.

Resources

[1] Vinanzi, S., Patacchiola, M., et al. (2019). Would a Robot Trust You? Developmental Robotics Model of Trust and Theory of Mind. Royal Society of London. Philosophical Transactions B. Biological Sciences , 374(1771), 1-9. [20180032]. [2] Y. Gao, E. Sibirtseva, et al. (2019). Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction. In proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019, pp. 305-312, doi: 10.1109/IROS40897.2019.8967924.

Background

Python programming

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

0

Supervisor

Marta Romeo

Keywords

Degrees

Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Master of Science in Artificial Intelligence
Master of Science in Human Robot Interaction
Master of Science in Robotics