Multi-Robot Collaboration for Trustworthy Lifelong Learning

Robots require lifelong learning to acquire, refine, and translate knowledge from new experiences. However, by making decisions based on information that is acquired continuously, as opposed to learning from large-scale datasets that are available a priori, lifelong learning raises significant challenges for the assurance of trustworthy AI: Homogeneous data acquired continually through insufficiently diverse experiences can lead to biased decisions (e.g., a robot with experience limited to households cannot straightforwardly translate its prior knowledge when exposed to, e.g., public spaces).

To ensure that a robot's reasoning is based on evidence from heterogenous experiences, distributed learning, e.g., federated learning, allows multiple robots expose to diverse scenarios to collaboratively share their individual knowledge. While distributed learning enables robots to think 'outside of the box', a risk remains that homogenous subgroups of robots instil systematic bias in the fused experience of the heterogenous cohort.

This project will analyse the source of bias in distributed learning, and will develop novel algorithms that mitigate bias during the aggregation of experiences from multiple robots, while preserving fairness and ensuring data privacy.